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ISSN: 1978 - 8282 Tangerang - Indonesia P R O C E E D I N G S

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Page 1: Proceedings ICCIT 09

ISSN: 1978 - 8282

Tangerang - Indonesia

P R O C E E D I N G S

Page 2: Proceedings ICCIT 09

Published by

CCIT Journal, Indonesia

The Association of Computer and Informatics Higher – Learning Instituition in Indonesia (APTIKOM) and

STMIK Raharja Tangerang Section

Personal use of this material is permitted. However, permition on reprint/republish this material for

advestising or promotional or for creating new collective work for resale or redistribution to servers or lists,

or to reuse any copyrighted component of this work in other works must be obtained form the Publisher

CD – Conference Proceedong

CCIT Catalog Number:

ISSN: 1978 – 8282

Technically Co-Sponsored by

National Council Informatioan Techonology Of Indonesia

Raharja Enrichment Centre (REC)

The Institutional of Information Communication and Technology

Digital Syinztizer Laboratory of Computers system Processing

Design and typeset by:

Sugeng Widada & Lusyani Sunarya

Page 3: Proceedings ICCIT 09

Steering Committee

- Message from Steering Committee 1

Programme Committee 4

- Message from Programme Committee 5

Organizing Committee 9

- Message from Organizing Committee 10

Paper Participants 13

Revievwers 17

- Panel of Reviewers 18

Keynote Speeches 19

- Richardus Eko Indrajit, Prof 20

- Rahmat Budiarto, Prof 31

Paper 32

Author Index 284

Schedule 286

Location 288

Contents

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Steering CommitteeSteering CommitteeSteering CommitteeSteering CommitteeSteering Committee

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Chairman:R. Eko Indrajit, Prof.(ABFI Institute, Perbanas)

Message from Steering Committee

Welcome Speech from the Chairman

The honorable ladies and gentleman, on behalf of all Programming Committee and Steering Committee, I

would like to welcome you all to the International Conference on Creative Communication and Innovative

Technology.

It is indeed a great privilege for us to have all of you here joining this international gathering that is professionally

organized by Perguruan Tinggi Rahardja. The ICCIT-09 has an ultimate objective to gather as many creative

ideas as possible in the field of information communication and technology that we believe might help the

country in boosting its economic development.

Since we are strongly confidence in the notions saying that great innovations are coming from the great

people, we have decided to invite a good number of young scholars originating from various campuses all

over the countries to join the gathering. It is our wish that the blend between new and old generations, wisdom

of legacy and the emerging modern knowledge, the proven technology and the proposed enabling solutions,

can lead to the invention of new products and services that will bring benefits to the society at large.

Let me use this opportunity to thank all participants who have decided to share their knowledge in this

occasion. Also a great appreciation I would like to express to the sponsors and other stakeholders who have

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Message from Steering Committee

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joined the committee to prepare and to launch such initiative successfully. Without your helps, it is impossible

to have the ICCIT-09 internationally commenced. Last but not least, this fantastic gathering will not come true

without the great efforts of Perguruan Tinggi Rahardja. From the deepest down of my heart, please allow me

to extend our gratitude to all management and staffs involved in the organizing committee.

I wish all of you have a great sharing moment. And I hope your stay in Tangerang, one of the industrial satellite

city of Jakarta, can bring the unforgettable experience to remember.

Thank you.

Prof. Richardus. Eko IndrajitHasibuan, Ph.DChairman of ICCIT 2009August 8, 2009

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Members:Tri Kuntoro Priyambodo, M.Sc.

(Gajah Mada University)

Members:Yusuf Arifin, MT.(Pasundan University)

Members:Once Kurniawan, MM.

(Bunda Mulia University)

Members:Rangga Firdaus, M.Kom.

(Lampung University)

Co Chairman:Zainal A. Hasibuan, Ph.D

(University of Indonesia)

Chairman:R. Eko Indrajit, Prof.(ABFI Institute, Perbanas)

Members:Tommy Bustomi, M.Kom.

(STMIK Widya Ciptadarma)

Members:Achmad Batinggi, MPA.

(STIMED Nusa Palapa)

Members:Philipus Budy Harianto(University Sains Technology

Jayapura)

Members:Arief Andi Soebroto, ST.

(Brawijaya University)

Steering Committee

Page 8: Proceedings ICCIT 09

Programme CommitteeProgramme CommitteeProgramme CommitteeProgramme CommitteeProgramme Committee

Page 9: Proceedings ICCIT 09

Dear Friends and Colleagues

On behalf of the Organizing Committee, we are pleased to welcome you to International Conference OnCreative Communication And Innovative Technology 2009 (ICCIT’09).The annual event of ICCIT’09 has proven to be an excellent forum for scientists, research, engineers anindustrial practitioners throughout the world to present and discuss the latest technology advancement as wellas future directions and trends in Industrial Electronics, and to set up useful links for their works. ICCIT’09 isorganized by the APTIKOM (The Association of Computer and Informatics Higher-Learning Institutions inIndonesia).ICCIT’09 received overwhelming responses with a total of 324 full papers submission from 40 countries/regions. All the submitted papers were processed by the Technical Program Committee which consists of theone chair, 3 co-chair and 18 track chairs who are worldwide well known experts with vast professionalexperience in various areas of the conference. All the members worked professionally, responsibly and dili-gently in soliciting expert international reviewers. Their hard working has enabled us to put together a verysolid technical program for our delegates. The technical program includes 36 papers for presentations in 36oral sessions and 2 interactive session. Besides the parallel technical session, there are also 2 keynote speechesand 3 distinguished invited lectures to be delivered by eminent professor and researchers. These talks willaddress the state-of-the-art development and leading-edge research activities in various areas of industrialelectronics.We are indeed honored to have Professor Richardus Eko Indrajit of APTIKOM (The Association of Com-puter and Informatics Higher-Learning Institutions in Indonesia), Professor Rahmat Budiarto of Universiti

Message from Programme Committee

Untung Rahardja, M.T.I.(STMIK Raharja, Indonesia)

5

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Message from Organizing Committee

Sains Malaysia, Professor Professor Suryo Guritno of Gadjah Mada University, Indonesia, as the keynotespeakers for ICCIT’09. Their presence would undoubtedly act prestige to the conference as they are thegiants in their respective fields. We would like to express our sincere appreciation to all the 3 keynote speakerand the 7 distinguished invited lectures speakers for their contribution and supports to ICCIT’09.A CD-ROM containing preprints of all paper schedule in the program and Abstract Book will be provided atthe conference to each registered participant as part of the registration material. The official conference pro-ceedings will be published by ICCIT’09 and included in the ICCIT Xplore Database.

We understand that many delegates are here in Tangerang Banten for the first time. We would like to encour-age you to explore the historical and beautiful sight of Tangerang Banten during you stay. To make thisconference more enjoyable and memorable. During the conference, a travel agent will provide on-site post-conference tour service to our delegates to visit historical sites. The conference will also organize technicaltours to the famous higher educational and research institution STMIK Raharja, one of the organizers.

On behalf of the Organizing Committee, we would like to thank all the organizers of the special session andinvite session and the numerous researchers worldwide who helped to review the submitted papers. We arealso grateful to the distinguished International Advisory Committee members for their invaluable supports andassistances. We would like to gladly acknowledge the technical sponsorship provided by the APTIKOM(The Association of Computer and Informatics Higher-Learning Institutions in Indonesia) and PerguruanTinggi Raharja Tangerang Banten Indonesia.We hope that you will find your participant in ICCIT’09 in Tangerang Banten stimulating, rewarding, enjoy-able and memorable.

Ir. Untung Rahardja M.T.IProgramme Committee of ICCIT 2009August 8, 2009

6

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Programme Committee

Arif Djunaidy, Prof.(Sepuluh November

Institute of Technology Indonesia)

Abdul Hanan Abdullah, Prof.(University Technology Malaysia)

Djoko Soetarno, Ph.D(STMIK Raharja, Indonesia)

Marsudi W. Kisworo, Prof.(Swiss-German University,

Indonesia)

K.C. Chan, Prof.(University of Glasgow,

United Kingdom)

Jazi Eko Istiyanto, Ph.D(Gajah Mada University,

Indonesia)

Edi Winarko, Ph.D(Gajah Mada University, Indonesia)

Iping Supriyana, Dr.(Bandung Institute of Technology,

Indonesia)

E.S. Margianti, Prof.(Gunadarma University,

Indonesia)

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Stepane Bressan, Prof.(National University of

Singapore)

Rahmat Budiarto, Prof.(University Sains Malaysia)

Suryo Guritno, Prof.(Gajah Mada University,

Indonesia)

Y. Sutomo, Prof.(STIKUBANK University,

Indonesia)

Wisnu Prasetya, Prof.(Utrecht Unversity, Netherland)

Untung Rahardja, M.T.I.(STMIK Raharja, Indonesia)

Susanto Rahardja, Prof.(Nanyang TechnologicalUniversity Singapore)

Thomas Hardjono, Prof.(MIT, USA)

T. Basaruddin, Prof.(University of Indonesia)

Programme Committee

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Organizing CommitteeOrganizing CommitteeOrganizing CommitteeOrganizing CommitteeOrganizing Committee

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It’s a great pleasure to welcome everyone to The International Conference on Creative Communication and

Innovative Technology 2009 (ICCIT-09). It is being held in the campus of Raharja Institution is a credit to

Banten and which emphasizes the global nature of both ICCIT and our networking research community.

ICCIT is organized by Raharja Institution together with APTIKOM (The Association of Computer and

Informatics Higher-Learning Institutions in Indonesia). We hope that this conference facilitates a stimulating

exchange of ideas among many of the members of our international research community.

ICCIT has been made possible only through the hard work of many people. It is offers an exceptional forum

for worldwide researchers and practitioners from academia, industry, business, and government to share their

expertise result and research findings in all areas of performance evaluation of computer, telecommunications

and wireless systems including modeling, simulation and measurement/testing of such systems.

Many individuals have contributed to the success of this high caliber international conference. My sincere

appreciation goes to all authors including those whose papers were not included in the program. Many thanks

to our distinguished keynote speakers for their valuable contribution to the conference. Thanks to the program

Message from Organizing Committee

General Char;Po. Abas Sunarya, M.Si.

(STMIK Raharja, Indonesia)

10

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Message from Organizing Committee

committee members and their reviewer for providing timely reviews. Many thanks to the session chairs for

their efforts. Thanks are also due to FTII, APJI, ASPILUKI, APKOMINDO, MASTEL, IPKIN and AINAKI,

for her fine support.

Finally, on behalf of the Executive and Steering Committees of the International Conference on Creative

Communication and Innovative Technology, ICCIT-09, and the Society for Modeling and The Association of

Computer and Informatics Higher-Learning Institutions in Indonesia (APTIKOM), I invite all of you to be us

in Raharja Institution, at ICCIT -09.

Drs. Po. Abas Sunarya, M. Si.

General Chair, ICCIT-09

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Organizing Committee

Members:Sugeng Santoso, S.Kom.

Members:Padeli, S.Kom.

Members:Augury El Rayeb, M.MSi.

Members:Eko Prasetyo Windari

Karso, Ph.D

Members:Euis Sitinur

Aisyah, S.Kom.

Co Chairman:Sunar Abdul Wahid, Dr.

Members:Lusyani Sunarya, S.Sn.

Members:Junaidi, S.Kom.

Members:Maria Kartika, SE.

Members:Muhamad Yusup, S.Kom.

Members:Mukti Budiarto, Ir

Co Chairman:Henderi, M.Kom.

Chairman:Po. Abas Sunarya, M.Si.

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Paper ParticipantsPaper ParticipantsPaper ParticipantsPaper ParticipantsPaper Participants

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Paper Participant

Gede Rasben Dantes- Doctoral Student in Computer Science Department, University of Indonesia

Widodo Budiharto, DjokoPurwanto, Mauridhi Hery Purnomo- Electrical Engineering Department Institue Technology Surabaya

Untung Rahardja, Valent- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Diyah Puspitaningrum, Henderi- Information System, Faculty of Computer Science

Wiwik Anggraeni, Danang Febrian- Information System Department, Institut Teknologi Sepuluh Nopember

Aan Kurniawan, Zainal A. Hasibuan- Faculty of Computer Science, University of Indonesia

Untung Rahardja, Edi Dwinarko, Muhamad Yusup- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia- GADJAH MADA UNIBERSITYFaculty of Mathematics and Natural SciencesYogyakarta,

Sarwosri, Djiwandou Agung Sudiyono Putro- Department of Informatics, Faculty of Information Technology- Institute of Technology Sepuluh Nopember

Chastine Fatichah, Nurina Indah Kemalasari- Department, Faculty of Information Technology- Institut Teknologi Sepuluh Nopember, Kampus ITS Surabaya

Untung Rahardja, Jazi Eko Istiyanto- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia- GADJAH MADA UNIVERSITY Yogyakarta, Republic of Indonesia

Bilqis Amaliah, Chastine Fatichah, Diah Arianti- Informatics Department – Faculty of Technology Information- Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia

Tri Pujadi- Information System Department – Faculty of Computer Study Universitas Bina Nusantara Jl. Kebon Jeruk Raya No. 27, Jakarta Barat 11530 Indonesia

Untung Rahardja, Retantyo Wardoyo, Shakinah Badar- Faculty of Information System, Raharja University Tangerang, Indonesia- Faculty of Mathematics and Natural Science, Gadjah Mada University Yogyakarta, Indonesia- Faculty of Information System, Raharja UniversityTangerang, Indonesia

14

Page 19: Proceedings ICCIT 09

Paper Participant

15

Henderi, Maimunah, Asep Saefullah- Information Technology Department – Faculty of Computer Study STMIK Raharja Jl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

Yeni Nuaraeni- Program Study Information Technology University Paramadina

Sfenrianto- Doctoral Program Student in Computer Science University of Indonesia

Asep Saefullah, Sugeng Santoso.- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Henderi, Maimunah, Aris Martono- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

M. Tajuddin, Zainal Hasibuan, Abdul Manan, Nenet Natasudian, Jaya- STMIK Bumigora Mataram West Nusa Tenggara- Indonesia University- PDE Office of Mataram City- ABA Bumigora Mataram

Ermatita, Edi Dwinarko, Retantyo Wardoyo- Information systems of Computer science Faculty Sriwijaya University (Student of Doctoral Program Gadjah Mada university)- Computer Science of Mathematics and Natural Sciences Faculty Gadjah Mada University

Junaidi, Sugeng Santoso, Euis Sitinur Aisyah- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Ermatita, Huda Ubaya, Dwiroso Indah- Information systems of Computer science Faculty Sriwijaya University (Student of Doctoral Program Gadjah Mada university)- Computer Science Faculty of Sriwijaya University Palembang-Indonesia.

Mauritsius Tuga- Jurusan Teknik Informatika Universitas Katolik Widya Mandira Kupang

Padeli, Sugeng Santoso- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

M. Givi Efgivia, Safarudin, Al-Bahra L.B.- Staf Pengajar STMIK Muhammadiyah Jakarta- Staf Pengajar Fisika, FMIPA, UNHAS, Makassar- Staf Pengajar STMIK Raharja, Tangerang

Primantara, Armanda C.C, Rahmat Budiarto, Tri Kuntoro P.- School of Computer Sciences, Univeristi Sains Malaysia, Penang, Malaysia- School of Computer Science, Gajah Mada University, Yogyakarta, Indonesia

Page 20: Proceedings ICCIT 09

Paper Participant

16

Hany Ferdinando, Handy Wicaksono, Darmawan Wangsadiharja- Dept. of Electrical Engineering, Petra Christian University, Surabaya - Indonesia

Untung Rahardja, Hidayati- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Dina Fitria Murad, Mohammad Irsan- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Asep Saefullaf, Augury El Rayeb- STMIK RAHARJA Raharja Enrichment Centre (REC) Tangerang - Banten, Republic of Indonesia

Richardus Eko Indrajit- ABFI Institute, Perbanas

Azzemi Arifin, Young Chul Lee, Mohd. Fadzil Amiruddin, Suhandi Bujang, Salizul Jaafar, NoorAisyah, Mohd. Akib- AKIB#6#System Technology Program, Telekom Research & Development Sdn. Bhd., TMR&D Innovation Centre, Lingkaran Teknokrat Timur, 63000 Cyberjaya, Selangor Darul Ehsan, MALAYSIA Division of Marine Electronics and Communication Engineering, Mokpo National Maritime University (MMU) 571 Chukkyo-dong, Mokpo, Jeonnam, KOREA 530-729

Sutrisno- Departement of Mechanical and Industrial Engineering, Gadjah Mada University, Jl. Grafika 2 Yogyakarta. 52281- Faculty of Mathematics and Natural Sciences, Gadjah Mada University,- Departement of Geodetical Engineering, Gadjah Mada University,

Saifuddin Azwar, Untung Raharja, Siti Julaeha- Faculty Psychology, Gadjah Mada University Yogyakarta, Indonesia- Faculty of Information System Raharja University Tangerang, Indonesia

Henderi, Sugeng Widada, Euis Siti Nuraisyah- Technology Department – Faculty of Computer Study STMIK Raharja Jl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

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ReviewersReviewersReviewersReviewersReviewers

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Panel of Reviewers

Abdul Hanan Abdullah, Prof.Universiti Teknologi Malaysia

Arif Djunaidy, Prof.Sepuluh November Institute of Technology,Indonesia

Djoko Soetarno, Ph.DSTMIK Raharja, Indonesia

Edi Winarko, Ph.DGajah Mada University, Indonesia

E.S. Margianti, Prof.Gunadarma University, Indonesia

Iping Supriyana, Dr.Bandung University of Technology, Indonesia

Jazi Eko Istiyanto, Ph.DGajah Mada University, Indonesia

K.C. Chan, Prof.University of Glasgow, United Kingdom

Marsudi W. Kisworo, Prof.Swiss-German University, Indonesia

Rahmat Budiharto, Prof.Universiti Sains Malaysia

Stephane Bressan, Prof.National University of Singapore

Suryo Guritno, ProfGajah Mada University, Indonesia

Susanto Rahardja, Prof.Nanyang Technologycal University, Singapore

T. Basaruddin, Prof.University of Indonesia,

Thomas Hardjono, Prof.MIT, USA

Untung Rahardja, M.T.I.STMIK Raharja, Indonesia

Wisnu Prasetya, Prof.Utrecht University, Netherland

Y. Sutomo, Prof.STIKUBANK University, Indonesia

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Saturday, August 8, 200913:30 - 13:50 Room M-AULA

DIGITAL SCHOOL: Expediting Knowledge Transfer and Learning throughEffective Use of Information and Communication Technology within Education

System of Republic Indonesia

Richardus Eko Indrajit, Prof.(APTIKOM)

Keynote Speech

AbstractThe involvement of Information and Communication Technology (ICT) within the educational system has been widelydiscussed and implemented by various scholars and practitioners. A good number of cases have shown that the effectiveuse of such technology can bring positive and significant improvement to the quality of learning deliveries. For a countrywhich believes that a serious development on ICT for education system could gain some sorts of national competitiveadvantage, a series of strategic steps has been undergone. Such effort is started from finding the strategic role and contextof ICT within the country’s educational system, followed by defining the architectural blue print of the various ICTimplementation spectrum and developing an implementation plan framework guideline. This article proposes one per-spective and approach on how the ICT for education should be developed within the context of Indonesia’s educationalsystem.

Schools in IndonesiaAs the biggest archipelago country in the world, Indone-sia consists of more than 18,000 islands nationwide. In2005, there are more than 230 million people living in this 5million square meter area where almost two third of it iswater. The existence of 583 languages and dialects spokenin the country is the result of hundreds of ethic divisionssplit up by diverse separated island. According to statis-tics, 99 million of Indonesia population are labors with45% of them works in agriculture sector. Other data sourcealso shows that 65% of total population are within pro-ductive age, which is 15-64 years old. The unbalancedregion development since the national independent’s dayof August 17th 1945 has made Java as the island with thehighest population density (average of 850 people per

square meter), comparing to the nation average of 100people per square meter. It means that almost 60% of totalIndonesia population live in this island alone1.

In the year 2004, the number of formal education institu-tions (schools) in the country – ranging from primary schoolto universities – has succeeded 225,000 institutions. Thereare approximately 4 million teachers who are responsiblefor more than 40 million students nationwide2. Note thatalmost 20% of the schools still have problems with elec-tricity as they are located in very remote area. For the pur-pose of leveraging limited resources and ensuring equalyet balance learning quality growth of the society, thegovernment adopts a centralized approach of managing

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education system as all policies and standards are beingset up by the Department of National Education lead by aMinister of Education3.

ICT in Education InstitutionThe involvement of ICT (Information and CommunicationTechnology) within education institution in Indonesiastarted from the higher-learning organization such as uni-versity and colleges. As the rapid development of suchtechnology in the market, several state universities andprominent colleges that have electrical engineering relatedfields introduced what so called as computer science pro-gram of study4. At that time, most of the computers wereused for two major purposes: s organizations in takingcare of their academic administrations, and supporting stu-dents conducting their research especially for the purposeof finishing their final project as a partial requirement to beawarded a bachelor degree. Currently, in the existence of 7million fixed telephone numbers and 14 million mobilephone users5, there are at least 12.5 million of internet us-ers in Indonesia6. Data from May 2005 has shown thatthere are more than 21,762 local domain name (.id) with thetotal accumulative of IPv6 address of 131,0737. From allthese domain, there are approximately 710 domains repre-senting education institutions8 (e.g. with the “.ac.id” sub-domain). It means that only less than 0.5% of Indonesianschools that are “ICT literate” – a ration that is consideredvery low in Asia Pacific region.

History has shown that a significant growth of ICT in edu-cation started from the commencement of the first ICT re-lated ministry, namely Ministry of Communication and In-formation in 20019. Through a good number of efforts andsocialization programs supported by private sectors, aca-demicians, and other ICT practitioners, a strategic planand blueprint of ICT for National Education System hasbeen produced and announced in 2004 by the collabora-tion of three ministries which are: Ministry of Communica-tion and Information, Department of National Education,and Department of Religion10.

The National Education SystemIndonesia’s national education system is defined and regu-lated by the UU-Sisdiknas RI No.20/2003 (Undang-UndangSistem Pendidikan Nasional Republik Indonesia)11. Thislast standard has been developed under the new paradigmof modern education system that is triggered by new re-quirements of globalization. All formal education institu-tions – from primary schools to universities – have to de-velop their educational system based on the philosophy,principles, and paradigms stated in this regulation.

Based on the national education system that has beenpowered by many discourses from Indonesia’s education

experts, the conceptual architecture of an education insti-tution can be illustrated through the following anatomy.

Vision, Mission, and ValueEvery school has its own vision and mission(s) in the so-ciety. Most of them are related to the process of knowl-edge acquisition (learning) for the purpose of increasingthe quality of people’s life. As being illustrated above, thevision and mission(s) of an education institution is verydepending upon the needs of stakeholders that can bedivided into 7 (seven) groups, which are (Picture 1):

Picture 1: The Conceptual Architecture of EducationalSystem

1. Owners and commissioners – who are coming fromvarious society, such as: religious communities,political organizations, education foundation,government, private sectors, etc.;

2. Parents and Sponsors – who are taking an activeportion as the parties that decide to which schoolstheir children or employees should be sent to;

3. Students and Alumni – who are at one aspect beingconsidered as the main customers or subject ofeducation but in other perspective represent output/outcome’s quality of the institution;

4. Management and Staffs – who are the parties that runeducation organization and manage resources toachieve targeted goals;

5. Lecturers and Researchers – who are the main sourceof institution’s most valuable assets which areintellectual property assets;

6. Partners and Industry – who are aliening with theinstitutions to increase practical knowledgecapabilities of the institution graduates; and

7. Government and Society – who are setting regulationand shaping expectation for ensuring qualityeducation being delivered.

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Four Pillars of the Education SystemThrough depth analysis of various performance indica-tors chosen by diverse education management practitio-ners – backing up also by a good number of research byacademicians on the related fields – there are at least 4(four) aspects or components that play important roles indelivering quality educations. Those four pillars are:

1. Content and Curriculum – the heart of the educationlies on the knowledge contained (=content) within theinstitution communities and network that are structured(=curriculum) so that it can be easily and effectivelytransferred and acquired by students;

2. Teaching and Research Delivery – the arts onacquiring knowledge through various learningactivities that promote cognitive, affective, andpsychomotor competencies transfers;

3. Human Resource and Culture – by the end of the day,human resource are the people who are having andwilling to share all knowledge they have to other peoplewithin a conducive academic environment and culturethrough appropriate arrangements; and

4. Facilities and Network – effective and qualityeducation deliverables nowadays can only be donethrough adequate existence of facilities andinstitutional network (i.e. with all stakeholders).

Some of institutions consider these four pillars as criticalsuccess factors while some of them realize that such com-ponents are the minimum resources (or even a businessmodel) that they have to carefully manage14 as educatorsor management of education institutions. Note that thereare some local regulations that rule the education institu-tion to have minimum physical assets or other entitieswithin specific ratio to be able to operate in Indonesia.Such requirements will be checked by the governmentduring the process of building new school and in the on-going process of the school operations as quality control.

Institution Infrastructure and SuperstructureFinally, all of those vision, missions, objectives, KPIs, andpillars, are being built upon a strong holistic institutioninfrastructure and superstructure foundation. It consistsof three components that build the system, which are:

1. Physical Infrastructure – consist of all assets such asbuilding, land, laboratory, classes, technology, sportscenter, parking space, etc. that should be required toestablish a school15;

2. Integrated Services – consist of a series of processesintegrating various functions (e.g. strategic tooperational aspects) exist in school to guaranteeeffectiveness of education related services; and

3. Quality Management System – consist of all policies,

standards, procedures, and governance system thatare being used to manage and to run the institution toguarantee the quality16.

ICT Context on EducationWhile trying to implement these education principles, allstakeholders believe that information is everything, in thecontext of:

• Information is being considered as the rawmateinformation are mandatory;

• Information is something that is very crucial for managing and governing purposes è since thesustainability of a school can be seen from all dataand/or relevant and reliable information are very important; and

• Information is a production factor in educationservices è since in every day’s transactions,interaction, should be well managed.

ICT Context and Roles in National Education SystemBased on the defined National Education System, thereare 7 (seven) context and roles of ICT within the domain,which are (Picture 2):1. ICT as Source of Knowledge;2. ICT as Teaching Tools and Devices;3. ICT as Skills and Competencies;4. ICT as Transformation Enablers;5. ICT as Decision Support System;6. ICT as Integrated Administration System; and7. ICT as Infrastructure.

Picture 2: The Role and Context of ICT in Education

It can be easily seen that these seven context and roles arederived from the four pillars and three institution infra-structure/superstructure components within the nationaleducation system architectural framework. Each contextand/or role supports one domain on the system17. Thefollowings are the justification on what and why such con-text and roles exist.

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ICT as Source of KnowledgeThe invention of internet – the giant network of networks– has shift on how education and learning should be done

Objectives and Performance IndicatorsIn order to measure the effectiveness of series of actionstaken by institution in order to achieve their vision andmissions, various objectives and performance indicatorsare being defined. Previously, for all government-ownedschools, the measurements have been set up by the states.But nowadays, every education institution is given a fullright to determine their control measurements as long as itdoes not violate any government regulation and educa-tion principles (and ethics)12. Good selection of indicatorsportfolio can represent not just only the quality level ofeducation delivery status, but also the picture ofsustainability profile of the institution13.

nowadays. As more and more scholars, researchers, andpractitioners are being connected to internet, a cyberspacehas been inaugurated as source of knowledge. In otherwords, ICT has enabled the creation of new world whereknowledge are being collected and stored. Several prin-ciples that are aligned with the new education system para-digm are as follows18:• New knowledge are being found at a speed ofthought today, which make any scholar has to be able torecognize its existence è through ICT (e.g. internet), suchknowledge can be easily found and accessed in no time;• Most of academicians, researchers, scholars, stu-dents, and practitioners disclose what they have (e.g. data,information, and knowledge) through the internet so thatmany people in other parts of the world can take benefitout of it è through ICT (e.g. website, database), all thosemultimedia formats (e.g. text, picture, sound, and video)can be easily distributed to other parties; and• New paradigm of learning states that the sourceof knowledge is not just coming from the assigned lectureror textbooks of a course in a class, but rather all experts inthe fields and every reference found in the world are thesource of knowledge è through ICT (email, mailing list,chatting, forum) every student can interact with any lec-turer and can have accessed to thousands of libraries forreferences.

With respect to this context, at least there are 7 (seven)aspects of application any education institution stake-holder should be aware of, which are:

1. Cyber Net Exploration – how knowledge can be found,accessed, organized, disseminated, and distributedthrough the internet19;

2. Knowledge Management – how knowledge in manyforms (e.g. tacit and explicit) can be shared through

various approaches;3. Community of Interests Groupware – how community

of lecturers, professors, students, researchers, management, and practitioners can do collaboration,cooperation, and communication through meeting incyber world;

4. Institution Network – how school can be a part of andaccess a network where its members are education institutions for various learning-based activities;

5. Dynamic Content Management – how data or contentare dynamically managed, maintained, and preserved;

6. Standard Benchmarking and Best Practices – howschool can analyze themselves by comparing theirknowledge-based acquisition with other education institutions worldwide and learning from their success;and

7. Intelligence System – how various scholars can havethe information regarding the latest knowledge theyneed without having to search it in advance.

ICT as Teaching Tools and DevicesLearning should become activities that are consideredenjoyable by people who involve. It means that the deliv-ery processes of education should be interesting so thateither teachers and students are triggered to acquire andto develop knowledge as they convenience. As suggestedby UNESCO, Indonesia has adopted the “Competence-Based Education System” that force the education institu-tion to create curriculum and to conduct delivery ap-proaches that promote not just cognitive aspect of com-petence, but also affective and psychomotor ones. Thereare several paradigm shifts that should be adapted relatedto teacher’s learning style to promote the principle (Pic-ture 3). The followings are some transformation that shouldbe undergone by all teachers in education institution20.

Picture 3: The Paradigm Change in Teaching Delivery

From above paradigm, it is clearly defined on how ICT canhelp teachers in empowering their delivery styles to the

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students and how students can increase their learningperformance. There are at least 17 (seventeen) applica-tions related to this matter as follows:

1. Event Imitation – using technology to createanimation of events or other learning subjectsrepresenting real life situation;

2. Case Simulation - enabling teachers and students tostudy and to perform “what if” condition in many casessimulation;

3. Multimedia Presentation – mixing various format oftexts, graphics, audio, and video to represent manylearning objects;

4. Computer-Based Training (CBT) – technology modulethat can help students to conduct independent study;

5. Student Learning Tools – a set of programs to helpstudents preparing and storing their notes,presentation, research works, and other learning related stuffs;

6. Course Management – an application that integratesall course related activities such as attendeesmanagement, materials deliverable, discussion forum,mailing list, assignments, etc.

7. Workgroup Learning System – a program that canfacilitate teachers and students group-basedcollaboration, communication, and cooperation;

8. Three-Party Intranet – a network that links teachers,students, and parents as main stakeholders ofeducation;

9. Examination Module – a special unit that can be usedto form various type of test models for learningevaluation purposes;

10. Performance Management System – software that canhelp teacher in managing student individual learningrecords and tracks for analyzing his/her specific studyperformance;

11. Interactive Smart Book – tablet PC or PDA-baseddevice that is used as intelligent book;

12. Electronic Board – a state-of-the-art board that acts asuser interface to exchange the traditional blackboardand whiteboard; and

13. Blogger – a software module that can help the teacherkeep track of student progress through their dailyexperience and notes written in the digital format.

ICT as Skills and CompetenciesSince teachers and students will be highly involved inusing many ICT-based application, the next context androle of ICT that should be promoted is its nature as a thingthat every teacher and student should have (e.g. skills andcompetencies). This digital literacy (or e-literacy) shouldbecome pre-requisites for all teachers and students whowant to get maximum benefit of ICT implementation in edu-cation system. In other words, a series of training program

should be arranged for teachers and range of preliminarycourses should be taken by students so that at least theyare familiar in operating computer-based devices and ap-plications21. To be able to deliver education and to learn inan effective and efficient way – by using ICT to add value– several tools and applications that should be well under-stood by both teachers and students are listed below:

1. Word Processing - witting software that allows thecomputer to resemble a typewriter for the purpose ofcreating reports, making assignments, etc.;

2. Spreadsheet - type of program used to perform variouscalculations, especially popular for mathematic,physics, statistics, and other related fields;

3. Presentation Tool – a software to be used for creatinggraphical and multimedia based illustration forpresenting knowledge to the audience;

4. Database - a collection of information that has beensystematically organized for easy access and analysisin digital format;

5. Electronic Mail - text messages sent through acomputer network to a specified individual or groupthat can also carry attached files;

6. Mailing List - a group of e-mail addresses that are usedfor easy and fast distribution of information to multiplee-mail addresses simultaneously;

7. Browser - software used to view and interact with resources available on the internet;

8. Publisher – an application to help people in creatingbrochures, banners, invitation cards, etc.;

9. Private Organiser - a software module that can serve asa diary or a personal database or a telephone or analarm clock etc.;

10. Navigation System – an interface that acts as basicoperation system that is used to control all computerfiles and resources;

11. Multimedia Animation Software - system that supportsthe interactive use of text, audio, still images, video,and graphics;

12. Website Development– a tool that can be used todevelop web-based content management system;

13. Programming Language – a simple yet effective programming language to help people in developing smallapplication module;

14. Document Management – a software that can be usedin creating, categorizing, managing, and storingelectronic documents;

15. Chatting Tool – an application that can be utilized bytwo or more individuals connected to Internet in having real-time text-based conversations by typingmessages into their computer; and

16. Project Management - an application software to helppeople in planning, executing, and controlling eventbased activities.

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ICT as Transformation EnablersAs the other industrial sectors, ICT in the education fieldhas also shown its capability to transform the way learn-ing is delivered nowadays. It starts from the facts thatsome physical resources can be represented into digital orelectronic forms type of resources22. Because most of edu-cation assets and activities can be represented by digitalforms23, then a new world of learning arena can be estab-lished and empower (or alternate) the conventional ones.There are some entities or applications of these transfor-mation impacts, which are:

1. Virtual Library - A library which has no physicalexistence, being constructed solely in electronic formor on paper;

2. E-learning Class - any learning that utilizes a network(LAN, WAN or Internet) for delivery, interaction, orfacilitation without the existence of physical class;

3. Expert System - computer with ‘built-in’ expertise,which, used by a non-expert in an education area as anexchange of a teacher or other professional in particular field (expert);

4. Mobile School – a device that can be used to processall transactions or activities related to student-schoolrelationships (e.g. course schedule, assignmentsubmission, grade announcement, etc.);

5. War Room Lab – a laboratory consists of computersand other digital devices directly linked to manynetwork (e.g. intranet, internet, and extranet) that canbe freely used by teachers or students for their variousimportant activities; and

6. Digital-Based Laboratory - a room or building thatoccupied by a good number of computers to be usedfor scientific testing, experiments or research throughdiverse digital simulation system.

ICT as Decision Support SystemManagement of school consists of people who are respon-sible for running and managing the organization. Accom-pany by other stakeholders such as teachers, researchers,practitioners, and owner, management has to solve manyissues daily related to education deliveries – especiallywith related to the matters such as: student complains,resource conflicts, budget requirements, government in-quiries, and owner investigation. They have also neededto dig down tons of data and information to back them upin making quality decisions24. With regard to this matter,several ICT applications should be ready and well imple-mented for them, such as:

1. Executive Information System - a computer-basedsystem intended to facilitate and support theinformation and decision making needs of seniorexecutives by providing easy access to both internal

and external information relevant to meeting thestrategic goals of the school;

2. Decision Support System - an application primarilyused to consolidate, summarize, or transformtransaction data to support analytical reporting andtrend analysis;

3. Management Information System - an informationcollection and analysis system, usually computerized,that facilitates access to program and participantinformation to answer daily needs of management,teachers, lecturers, or even parents; and

4. Transactional Information System – a reporting andquerying system to support managers andsupervisors in providing valuable informationregarding daily operational activities such as officeneeds inventory, student attendance, paymentreceived, etc.

ICT as Integrated Administration SystemThe Decision Support System that has been mentionedcan only be developed effectively if there are full inte-grated transaction system in the administration and op-erational levels. It means that the school should have anintegrated computer-based system intact. Instead of a “ver-tical” integration (for decision making process), this sys-tem also unites the four pillars of ICT context in someways so that a holistic arrangement can be made. The sys-tem should be built upon a modular-based concept so thatit can help the school to develop it easily (e.g. fit with theirfinancial capability) and any change in the future can beeasily adopted without having to bother the whole sys-tem. Those modules that at least should be developed are:

1. Student Management System – a program that recordsand integrates all student learning activities rangingfrom their detail grades to the specific daily progresses;

2. Lecturer Management System – a module that helpsthe school in managing all lecturer records and affairs;

3. Facilities Management System – a unit that managesvarious facilities and physical assets used foreducation purposes (e.g. classes, laboratories,libraries, and rooms), such as their schedules,allocations, status, etc.);

4. Courses Management System – a system that handlescurriculum management and courses portfolio whereall of the teachers, students, and facilities interact;

5. Back-Office System – a system that takes care all ofdocuments and procedures related to school’s records;

6. Human Resource System – a system that deals withindividual-related functions and processes such as:recruitment, placement, performance appraisal, training and development, mutation, and separation;

7. Finance and Accounting System – a system that takescharge of financial management records; and

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8. Procurement System – a system that tackles the dailypurchasing processes of the school.

ICT as Core InfrastructureAll of the six ICT contexts explained can not be or will notbe effectively implemented without the existence of themost important assets which are technologies themselves.There are several requirements for the school to have physi-cal ICT infrastructure so that all initiatives can be executed.In glimpse, these layers of infrastructure look like the sevenOSI layer that stack up from the most physical one to theintangible asset type of infrastructure. There are 9 (nine)components that are considered important as a part ofsuch infrastructure, which are:

1. Transmission Media – the physical infrastructure thatenables digital data to be transferred from one place toanother such as through: fiber optic, VSAT, cable sea,etc.;

2. Network and Data Communication – the collection ofdevices that manage data traffic in one or more network topology system(s);

3. Operating System – the core software to runcomputers or other microprocessor-based devices;

4. Computers – the digital-based processing devices thatcan execute many tasks as programmed;

5. Digital Devices – computer-like gadgets that can havea portion of capability as computers;

6. Programming Language – a type of instructions setthat can be structured to perform special task run bycomputers;

7. Database Management – a collection of digital filesthat store various data and information;

8. Applications Portfolio – a set of diverse software thathave various functions and roles; and

9. Distributed Access Channels – special devices thatcan be used by users to access any of the eight components mentioned.

Measurements of CompletenessEvery school in the country has been trying to implementmade, a performance indicator should be defined. The ba-sic indicator that can be used as measurement is portfoliocompleteness. The idea behind such measurement is tocalculate how many percent of the applications on each ofit reflects the completeness measurement (Picture 4). A“0% completeness” means that a school has not yet imple-mented any system while a “100% completeness” has ameaning that a school has been implementing all applica-tions portfolio25.

Picture 4: The Calculation Formula for Portfolio Com-pleteness

In the calculation above a weighting system is used basedon the principles that the existence of human resourcesand physical technologies are the most important things(people and tools) before any process can be done (Pic-ture 5)26. People means that they have appropriate compe-tencies and willingness to involve ICT in the educationprocesses while technology represents minimum existenceof devices and infrastructure (e.g. computers and internet).

Picture 5: Recommended Step-By-Step Implementation

Stakeholder-System Relationship FrameworkThe next important thing that should be addressed is theStakeholer-System Relationships Framework. It consistsof one-to-one relation between a system pillar and a stake-holder type – where shows that at least there is a majorstakeholder that concerns with the existence of a applica-tion type. The seven one-to-one relationships are (Picture6)27:

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1. Parent or sponsor of student will only select or favorthe school that has embraced ICT as one of educationtools;

2. Student will expect the school to use ICT intensivelyin learning processes;

3. Owner of the school should think how to transform theold conventional school into the new moderninstitution;

4. Teacher or lecturer must be equipped with appropriateskills and competencies to operate and use variousICT applications;

5. Employee of the school has no choice not to useintegrated ICT system for helping them doing everyday’s administration activities;

6. Management of the institution should use ICT to empower their performance especially in the process ofdecision making; and

7. Government of Indonesia has main responsibility toprovide the education communities with affordable ICTinfrastructure to be used for learning purposes.

Picture 6: Stakeholder-System Relationship Framework

Stakeholder Maturity LevelIt is extremely important – for a developing country likeIndonesia with relatively low e-literacy – to measure thematurity level of each stakeholder in education, especiallyafter realizing the existence between stakeholder and thesystem and among the stakeholders themselves. By adapt-ing the 0-5 level of maturity as used firstly by SoftwareEngineering Institute28, each stakeholder of the school canbe evaluated in their maturity (Picture 7).

In principle, there are 6 (six) level of maturity as follows:

0. Ignore – a condition where a stakeholder does notreally care about any issue related to ICT;

1. Aware – a condition where a stakeholder has somekind of attention to the emerging role of ICT ineducation but only rest in the mind;

2. Plan – a condition where a stakeholder has decided toconduct some actions in the future with favor to theICT existence;

3. Execute – a condition where a stakeholder is activelyusing ICT for daily activity;

4. Measure – a condition where a stakeholder appliesquantitative indicator as quality assurance of ICT use;and

5. Excel – a condition where a stakeholder has successfully optimized the use of ICT as its purposes.

Picture 7: Education Stakeholders Maturity Level Tabel

By crossing the six level of maturity with all seven stake-holders, it can be generated the more contextual condi-tional statements29 based on stakeholder’s nature.

Mapping into ICT-Education MatrixSo far, there two parameters or indicators that can showthe status of ICT for education development in Indonesia,which are: portfolio completeness and maturity level. Basedon the research involving approximately 7,500 schools inIndonesia – from primary school to the college level – theexisting status of ICT development can be described as(Picture 8):

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• Rookie – the status where majority of schools (73%)only implement less than 50% of complete applicationsand have average maturity level of stakeholders lessthan 2.5;

• Specialist – the status where 17% of schools has highmaturity level (more than 2.5) but only for implementing less than 50% of total application types;

• Generalist – the status where more than 50%applications have been implemented (or at least boughtby the schools) but with the maturity level of less than2.5 (approximately 9% of the schools are in this type);and

• Master – the status where more than 50% applicationtypes have been implemented with the maturity levelabove 2.5 (only 1% of schools fit with this idealcondition).

Picture 8: The ICT-Education Matrix

Also coming from the research, several findings show that:

• Most schools that are in a “master” type are located inJava Island and considered as “rich institution30”

• Most schools that are in “rookie” type are consideredas “self-learning entrepreneur” since their knowledgeto explore the possibilities to use ICT in education iscoming from reading the books, attending theseminars, listening the experts, and other sources;

• Most schools that are in “specialist” type are profiledschools31 that have pioneered themselves in using ICTfrom sometimes ago; and

• Most schools that are in “generalist” type are the onesthat receive one or more funding or helps from otherparties32.

The Plan Ahead

So far, there two parameters or indicators After understand-ing all issues related to the strategic roles of ICT within

Indonesia education system setting – and through depthunderstanding of the existing conditions – a strategic ac-tion can be planned as follows33:

• 2005-2007 – there should be 200 selected pilot schoolsthat have been successfully implemented allapplications portfolio with the high maturity level ofstakeholders (master class) spreading out in the 33provinces of Indonesia;

• 2007-2009 – these 200 schools have responsibilities todevelop 10 other schools per each so that 2,000 schoolsin 2009 that are in master level class;

• 2009-2010 – the same task apply to the new 2000schools so by 2010, approximately 20,000 schools canset the national standard of ICT in education (since italready covers almost 10% of total population).

References

[1] Computers as Mindtools for Schools – Engaging Criti-cal Thinking.

[2] E-Learning: An Expression of the Knowledge-Economy– A Highway Between Concepts and Practice.

[3] E-Learning Games: Interactive Learning Strategies forDigital Delivery.

[4] E-Learning: Building Successful Online Learning in YourOrganization – Strategies for Delivering Knowledgein the Digital Age.

[5] E-Learning and the Science of Instruction: ProvenGuidelines for Consumers and Designers of Multi-media Learning.

[6] Evaluating Educational Outcomes - Test, Measure-ment, and Evaluation.

[7] Implementasi Kurikulum 2004 – Panduan PembelajaranKBK.

[8] Integrating ICT in Education – A Study of SingaporeSchools.

[9] Konsep Manajemen Berbasis Sekolah (MBS) danDewan Sekolah.

[10] Manajemen Pendidikan Nasional: Kajian PendidikanMasa Depan.

[11] Manajemen Berbasis Sekolah – Konsep, Strategi, danImplementasi.

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[12] Paradigma Pendidikan Universal di Era Modern danPost Modern: Mencari Visi Baru atas Realitas BaruPendidikan Kita.

[13] Sistem Pendidikan Nasional dan PeraturanPelaksanaannya.

[14] Smart Schools – Blueprint of Malaysia EducationalSystem.

[15] Starting to Teach Design and Technology – A HelpfulGuide for Beginning Teachers.

[16] Teaching and Learning with Technology – An Asia-Pacific Perspective.

[17] The ASTD E-Learning Handbook.[18] Undang-Undang Republik Indonesia Nomor 20 tahun

2003 tentang Sistem Pendidikan Nasional.

Richardus Eko Indrajit, guru besar ilmukomputer ABFI Institute Perbanas, dilahirkan di Jakartapada tanggal 24 Januari 1969. Menyelesaikan studi pro-gram Sarjana Teknik Komputer dari Institut TeknologiSepuluh Nopember (ITS) Surabaya dengan predikat CumLaude, sebelum akhirnya menerima bea siswa dariKonsorsium Production Sharing Pertamina untukmelanjutkan studi di Amerika Serikat, dimana yangbersangkutan berhasil mendapatkan gelar Master of Sci-ence di bidang Applied Computer Science dari HarvardUniversity (Massachusetts, USA) dengan fokus studi dibidang artificial intelligence. Adapun gelar Doctor of Busi-ness Administration diperolehnya dari University of theCity of Manyla (Intramuros, Phillipines) dengan disertasidi bidang Manajemen Sistem Informasi Rumah Sakit. Gelarakademis lain yang berhasil diraihnya adalah Master ofBusiness Administration dari Leicester University (Leices-ter City, UK), Master of Arts dari the London School ofPublic Relations (Jakarta, Indonesia) dan Master of Phi-losophy dari Maastricht School of Management(Maastricht, the Netherlands). Selain itu, aktif pulaberpartisipasi dalam berbagai program akademis maupunsertifikasi di sejumlah perguruan tinggi terkemuka dunia,seperti: Massachusetts Institute of Technology (MIT),Stanford University, Boston University, George Washing-ton University, Carnegie-Mellon University, Curtin Uni-versity of Technology, Monash University, Edith-CowanUniversity, dan Cambridge University. Saat ini menjabat

sebagai Ketua Umum Asosiasi Perguruan TinggiInformatika dan Komputer (APTIKOM) se-Indonesia danChairman dari International Association of Software Ar-chitect (IASA) untuk Indonesian Chapter. Selain di bidangakademik, karir profesionalnya sebagai konsultan sistemdan teknologi informasi diawali dari Price Waterhouse In-donesia, yang diikuti dengan berperan aktif sebagaikonsultan senior maupun manajemen pada sejumlahperusahaan terkemuka di tanah air, antara lain: RenaissanceIndonesia, Prosys Bangun Nusantara, Plasmedia, the PrimeConsulting, the Jakarta Consulting Group, SoedarpoInformatika Group, dan IndoConsult Utama. Selama kuranglebih 15 tahun berkiprah di sektor swasta, terlibat langsungdalam berbagai proyek di beragam industri, seperti: bankdan keuangan, kesehatan, manufaktur, retail dan distribusi,transportasi, media, infrastruktur, pendidikan,telekomunikasi, pariwisata, dan jasa-jasa lainnya. Sementaraitu, aktif pula membantu pemerintah dalam sejumlahpenugasan. Dimulai dari penunjukan sebagai Widya IswaraLembaga Ketahanan Nasional (Lemhannas), yang diikutidengan beeperan sebagai Staf Khusus Bidang TeknologiInformasi Sekretaris Jendral Badan Pemeriksa Keuangan(BPK), Staf Khusus Balitbang Departemen Komunikasidan Informatika, Staf Khusus Bidang Teknologi InformasiBadan Narkotika Nasional, dan Konsultan Ahli DirektoratTeknologi Informasi dan Unit Khusus ManajemenInformasi Bank Indonesia. Saat ini ditunjuk oleh pemerintahRepublik Indonesia untuk menakhodai institusi pengawasinternet Indonesia ID-SIRTII (Indonesia Security IncidentResponse Team on Internet Infrastructure). Seluruhpengalaman yang diperolehnya selama aktif mengajarsebagai akademisi, terlibat di dunia swasta, dan menjalanitugas pemerintahan dituliskan dalam sejumlah publikasi.Hingga menjelang akhir tahun 2008, telah lebih dari 25 bukuhasil karyanya yang telah diterbitkan secara nasional danmenjadi referensi berbagai institusi pendidikan, sektorswasta, dan badan pemerintahan di Indonesia – diluarberagam artikel dan jurnal ilmiah yang telah ditulis untukkomunitas nasional, regional, dan internasional. Seluruhkaryanya ini dapat dengan mudah diperoleh melalui situspribadi http://www.eko-indrajit.com atau http://www.eko-indrajit.info. Sehari-hari dapat dihubungi melalui nomortelepon 0818-925-926 atau email [email protected].(Footnotes)1 Many people believe that more than 80% of economicand business activities are being conducted or/and hap-pened in this island.2 Taken from the annual report of the Department of Na-tional Education of Republic Indonesia at the end of year2004.3 In the previous time, such department was also takingcare of national culture affairs (e.g. the Department of Edu-cation and Culture).4 It started with the first batch of informatics students in

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Bandung Institute of Technology in 1984, followed bySepuluh November Institute of Technology Surabaya in1985, and then University of Indonesia in 1986, and GadjahMada University in 1987.5 According to PT Telkom Tbk. report on telecommunica-tion profile in early year of 2005.6 Data from APJII, the Association of National InternetProviders, with regards to number of internet users in midyear of 2005.7 Also taken from APJII website at http://www.apjii.or.id.8 Statistics from http://www.cctld.or.id.9 It was formed under President Megawati period as theembryo of today’s Department of Communication and Information Tech-nology that was formalized by President Susilo BambangYudhoyono.10 The involvement of Department of Religion is very im-portant as thousands of schools are owned by specificreligion-based communities.

11 UU (Undang-Undang) is the highest regulation form(act) of the national constitution system.12 As the education industry grows so fast, there are phe-nomena of commoditizing education’s products and services that might against governmentregulation and common ethics in promoting quality edu-cation.13 Unlike commercial company, never in mind of any edu-cator to close the schools in any circumstances.14 The Robert Kaplan’s concept of Balanced-Scorecard can be easily adapted inthe education institution by using these 4 (four) pillars asthe scorecard domains.15 There are minimum standard requirements set up by thegovernment that should be obeyed by anybody who wouldlike to form a school.16 Although the Indonesian government has formed sev-eral standards and guidance that can be used by the schoolfor these superstructure aspects, some of them have beenfollowing other international standards such asISO9001:200, Malcolm Balridge Quality Award, etc.17 This concept has been agreed to become the paradigmused to develop National ICT Blueprint for Education inIndonesia.18 The “Competence Based Curriculum” (CBC)– a new learning/education paradigm that should beadapted by all schools in Indonesia– is very much supporting the existence of ICT withineducation system.19 At least a skill to do advance search techniques is re-quired for this purpose.20 It is understood that the transformation can be onlydone if the school itself undergoes a series of fundamentalchange in paradigm, principles, and philosophy of manag-

ing today’s modern educational organization.21 Note that the government of Indonesia is in the middleof discussion on putting the e-literacy as a “must to have”skills and competencies for civil servants in the country,including teachers.22 It is also true with the blending between physical valuechain (a series of processes that require physical resources)and virtual value chain (a series of process that involvethe flow of digital goods).23 Don Tapscott refers this phenomenon as “digitalization”on his principle of “Digital Economy”.24 Having a good decision becomes something importantas government urges all organizations to implement a “goodgovernance” system.25 Note that the stakeholders aware the impacts on newtechnology to be included in the portfolio. But for the timebeing, it has been agreed that no new application shouldbe added to the portfolio until a further decision has beenmade based on the evaluation.26 The weights have been defined through a national con-sensus in implementing the blue print on ICT for educa-tion.27 The all assumptions being made are based on the trendand phenomena appear in the Indonesia market setting.28 It is highly used in many areas such as project manage-ment, ICT human resource development, IT governance,etc.29 Collection of statements that pictures the “mental con-dition” of a stakeholder in how their perceive the impor-tant of ICT in education.30 Meaning that such institution has strong financial re-sources.31 For example schools that are owned by religious com-munity or industrial-related business groups.32 Including from Microsoft PIL Program or other sourcessuch as USAID, ADB, JICA, etc.33 It has been approved and endorsed by the Ministry ofEducation and the Department of Communication and In-formation Technology.

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Keynote Speech

Name : RAHMAT BUDIARTOInstitution : NAv6 CENTRE USMPosition (Please underline) : Associate Professor. DrArea(s) of Expertise : COMPUTER NETWORK, AI

Brief Biodata:Rahmat Budiarto received B.Sc. degree from Bandung Institute of Technology in 1986, M.Eng, and

Dr.Eng in Computer Science from Nagoya Institute of Technology in 1995 and 1998 respectively.

Currently, he is an associate professor at School of Computer Sciences, USM. His research interest

includes IPv6, network security, Mobile Networks, and Intelligent Systems. He was chairman of

APAN Security Working Group and APAN Fellowship Committee (2005-2007).. He has been a JSPS

Fellow in the School of Computer and Cognitive Sciences of Chukyo University, Japan (2002). He

has published 26 International Journal papers, and more than 100 International and Local Conference

papers.

Rahmat Budiarto, Prof.(Universiti Sains Malaysia)

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{1Doctoral Student in Computer Science Department, University of Indonesia,Email: [email protected]}

AbstractAs further investigation on the Information and Communication Technology (ICT) investment especially in Indonesiashowed that a larger capital of investment does not automatically bring more benefit for the company, for exampleEnterprise Resource Planning (ERP) system implementation. The present research was aimed at developing a methodol-ogy for ERP Implementation which was fundamental problem for achieving a successful implementation. This methodol-ogy will be contained some factors that influenced ERP implementation success (technical or non-technical) as anactivity each phase. Because, some of methodologies that common used by consultant more concentrating on technicalfactors without considering non-technical factors. Non-technical factors were involved in the new proposed of ERPimplementation methodology, such as: top management commitment, support, and capability; project team composition,leadership, and skill; organizational culture; internal/external communication; organization maturity level; etc. Theconclusion of the study was expected to be useful for private or public sectors when implementing ERP in order to gainoptimal return value from their investment.

Keywords: Enterprise Resource Planning (ERP), Methodology, Return value.

Saturday, August 8, 200913:30 - 13:50 Room L-210

MULTI-FACTOR ENTERPRISE METHODOLOGY : AN APPROACHTO ERP IMPLEMENTATION

Gede Rasben Dantes

1. IntroductionEnterprise Resource Planning (ERP) is one of the integratedinformation systems that support business process andmanage the resources in organization. This system inte-grates a business unit with other business unit in the sameorganization or inter-organization. ERP is needed by orga-nization to support day to day activity or even to createcompetitive advantage.In the ERP implementation, a business transformation isalways made to align ERP business process and company’sbusiness strategy. This transformation consists ofcompany’s business process improvement, cost reduction,service improvement, and minimizing the effect on thecompany’s operation (Summer, 2004). Consequently, thereneeds to be an adjustment between the business processthat the ERP system has and the business process thatexists in the company to give value added for the com-pany.There are some ERP systems that are currently developed.In the study conducted by O’Leary (2000) it is shown thatSAP (System, Application, Product in Data Processing) isa system that has the largest market share in the world,

which is between 36% to 60%.Different from information systems in general, ERP is anintegration of hardware technology and software that hasa very high investment value. However, a larger capitalinvestment on ERP does not always give a more optimalreturn value to the company. Dantes (2006) found out thatin Indonesia, almost 60% of companies implementing theERP systems did not succeed in their implementations.While Trunick (1999) and Escalle et al. (1999) found thatmore than 50% of the companies implementing ERP in theworld failed to gain optimal return value.

Various studies have been conducted to find the keys toERP implementation success, while some other studies alsotry to evaluate it. Some factors that influence the organiza-tion to choose ERP system as a support, such as: indus-trial standards, government policies, creditor-bank poli-cies, socio-political conditions, organization maturity level,implementation approach or strategic reason. Finally, wefound that the choosing of ERP adoption does not exactlybase on organization requirement, especially in Indonesia.

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On the other hand, Xue et.al. (2005) found that organiza-tion culture & environment and technical aspects influ-enced ERP implementation success. Others research alsoshown that 50% of the companies implementing ERP failedto gain success (IT Cortex, 2003), while in China, only 10%of the companies gained success (Zhang et.al, 2003). Thesecontinuing study on the success of ERP implementationshow how critical ERP implementation is yet in IT invest-ment.

Related to this study, Niv Ahituv (2002) argues that ERPimplementation methodology is the fundamental problemin implementation success. In line with this, the presentresearch is aimed at developing ERP implementation meth-odology, taking into account the key success factor (tech-nical or non-technical factors) that will be included in ERPimplementation methodology.

2. Theoretical BackgroundOne of the major issues in ERP implementation is the ERPsoftware itself. What should come first, the company’sbusiness needs or the business processes available in theERP software? The fundamental invariant in system de-sign and implementation is that the final systems belongto the users.

A study by Deloitte Consulting (1999) indicated that go-ing live isn’t the end of ERP implementation, but “merelythe end of the beginning”. The radical changes in busi-ness practices driven by e-commerce and associatedInternet technologies are accelerating change, ensuringthat enterprise systems will never remain static.Because of the uniqueness of ERP implementation, meth-odologies to support ERP systems implementation are vi-tal (Siau, 2001, Siau and Tian 2001). A number of ERP imple-mentation methodologies are available in the marketplace.These are typically methodologies proposed by ERP ven-dors and consultants. We classify ERP methodologies intothree generations – first, second, and third generations(Siau, 2001). Each successive generation has a wider scopeand is more complex to implement.

Most existing ERP implementation methodologies belongto the first generation ERP methodologies. These method-ologies are designed to support the implementation of anERP system in an enterprise, and the implementation istypically confined to a single site. Methodologies such asAccelerated SAP (from SAP), SMART, and AcceleratedConfigurable Enterprise Solution (ACES) are examples offirst generation ERP implementation methodologies.Second generation ERP methodologies are starting toemerge. They are designed to support an enterprise-wideand multiple-site implementation of ERP. Different busi-

ness units can optimize operations for specific markets,yet all information can be consolidated for enterprise-wideviews. A good example is the Global ASAP by SAP, intro-duced in 1999. This category of methodologies supportsan enterprise-wide, global implementation strategy thattakes geographic, cultural, and time zone differences intoaccount.

Third generation ERP methodologies will be the next wavein ERP implementation methodologies. The proposed meth-odologies need to include the capability to support multi-enterprise and multiple-site implementation of ERP soft-ware so that companies can rapidly adapt to changingglobal business conditions, giving them the required agil-ity to take advantage of market or value chain opportuni-ties. Since more than one company will typically be in-volved. The methodologies need to be able to support theintegration of multiple ERP systems from different ven-dors, each having different databases. The multi-enter-prise architecture will need to facilitate the exchange ofinformation among business units and trading partnersworldwide. The ability to support web access and wirelessaccess is also important.

When we see more specific into some of methodologythat we review from literatures. All of them more concernabout technical factors with less considering of non-tech-nical factors into an ERP implementation methodology.As explained above, Niv Ahituv et.al. (2002) proposed anERP implementation methodology with collaborating Soft-ware Development Life Cycle (SDLC), Prototyping andSoftware Package. The methodology contains four phases,namely: selection, definition, implementation and opera-tion.In line with Niv Ahituv, Jose M. Esteves (1999) divided anERP Life cycle become five phases, such as: adoption,acquisition, implementation, use & maintenance, and evo-lution & retirement. And one of famous ERP product, SAPproposed well-known methodology namely AcceleratedSAP (ASAP) that contains five phases: project prepara-tion (change chapter, project plan, scope, project team or-ganization), business blueprint (requirement review for eachSAP reference structure item and define using ASAP tem-plates), realization (master lists, business process proce-dure, planning, development programs, training material),final preparation (plan for configuring the productionhardware’s capabilities, cutover plan, conduct end usertraining), go live & support (ensuring system performancethrough SAP monitoring and feedback).

However, Shin & Lee (1996) show that ERP life cycle con-tained three phases, such as: project formulation (initia-tive, analysis of need); application software package se-lection & acquisition (preparation, selection, acquisition);

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installation, implementation & operation.In general, all ERP implementation methodologies abovehave a similar concept. But there are only more concerningon technical factors than non-technical factors.

3. Research DesignMethodology is a fundamental problem on ERP implemen-tation (Juhani et. al, 2001). When the organizations weresuccessful in implementing ERP system, it can improve anorganization productivity and efficiency. The conceptualframework will be used in this research, describe in figure1.Variables on this research contain of independent vari-able, such as: ERP implementation success factors (X1..X2)(i.e. organization maturity level, implementation approach,top management commitment, organization culture, invest-ment value, etc.) and dependent variable is ERP implemen-tation success.Referring back to final product, this study used a literaturereview methodology. The developing ERP implementationmethodology is academic activities that need a theoreticalexploration and a real action. Furthermore, the planningand developing this methodology, we need to identifysome problems and doing a deep analysis for some factorsthat influenced ERP implementation success. These fac-tors can be used to develop a preliminary study of ERPimplementation methodology. The phases that have to bedone in this research are: justification of ERP implementa-tion success factors (technical or non-technical) from lit-eratures review, and the developing of preliminary model.4. Result and Discussion

In this study, we found out that some factors that influ-enced an ERP implementation success can be shown ontable 1. These factors (technical or non-technical) will beused to develop a new ERP Implementation methodology.Non-technical aspects were important thing that alwaysforgotten by organization when adopt ERP system as sup-port for their organization. A lot of companies were failedto implement ERP system because of it.

Ø ERP Implementation Success Factors

Related to the literature review which is focused on dis-cussion and need assessment for ERP implementation inprivate or public sector, we can conclude that some fac-tors influence the ERP implementation success, we canclassify into three aspects, namely: Organizational, Tech-nology and Country (External Organizational).

§ Organizational AspectsThe organizational aspect is an important role in ERP imple-mentation. Related to it, there are some activities that aresupposed to be done on ERP implementation methodol-ogy, such as: (1) identification of top management sup-port, commitment and capability; (2) identification ofproject team composition and leadership; (3) identifica-tion of business vision; (4) preparing of project scope,schedule and role; (5) identification of organization matu-rity level; (6) change management; (7) Business ProcessReengineering (BPR); (8) building of functional require-ment; (9) preparing of training program; (10) build a good

Figure 1. The conceptual framework for ERP implementation Methodology

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internal/external factor; and (11) identification an invest-ment budget.

§ Technology AspectsThis aspect contains software, hardware and ICT infra-structure. Technology aspect needs to be identified be-fore we implement ERP system. We can divide this aspectbecome certain activities that important for ERP implemen-tation methodology, such as: (1) identification of legacysystems; (2) software configuration; (3) choosing of imple-mentation strategy; (4) motivating of user involvement;(5) identification of hardware and ICT infrastructure; (6)identification of consultant skill; (7) data conversion; and(8) systems integration.

§ Country/External Organizational AspectsERP implementation as Enterprise System is very impor-tant to consider a country or external organizational as-pects. Viewed from a literature review, we can describesome activities that support for ERP implementation meth-odology, such as: (1) identification of current economicand economic growth; (2) aligning with government policy,and (3) minimizing a political issue that can drive ERP imple-mentation.Some of activities that we need to give a stressing from anexplanation above such as: organization maturity level andbusiness process. Organization maturity level is impor-tant aspect before chosen one of ERP product that will be

Table 1. Factors that influence an ERP Implementation Success

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adopted by organization to support their operational(Hasibuan and Dantes, 2009). It can divide into three lev-els, namely: operational, managerial and strategic level.Each level can define by considering a role of IS/IT to theorganization. For company that lied at operational level,the ERP system is only supporting a company operational.But the company that lied at strategic level can create acompetitive advantage for organization.

The other activity that also important is business process.It involves in ERP product as best practices. A lot of orga-nizations change the ERP business process to meet theirorganization business process. This affects to the failureof the ERP implementation. The changes in process give amore significant impact than the changes in technology.The process change in an organization has to be followedwith “management change” implementation. And the tech-nology changes usually will be followed by training toimprove the employees’ skill. Through this aspect, we candescribe two activities that give significant influence inthe development of ERP implementation methodology,namely: change management implementation, and identi-fying the alignment of the organization business processwith ERP business process.

Ø Comparison of ERP Implementation MethodologyA lot of ERP methodologies used by consultant/vendor toimplement this system. But, in this study we will comparesome of methodologies that common used, such as: Ac-celerated SAP (ASAP), ERP life cycle model by Shin &

Lee, Niv Ahituv et.al and Jose M. Esteves et.al. In general,all of methodologies have similar component, namely:selection phase (how we compare all of ERP product andchoose one of them that very suitable to organization re-quirement and budget), project preparation (this phase,we will prepare all of requirement for this project, such as:internal project team, consultant, project scope, functionalrequirement building, etc), implementation & development(how we will configure the software/ERP product to suitwith organization requirement), and the last part is opera-tional & maintenance (in this phase, system will be deployto production and try to support/maintenance it).

Normally, all of methodologies that used by consultantswere concerning about technical aspects without consid-ering non-technical aspects. Through this study we try toindentify some of non-technical aspects that influencedERP implementation success, such as: top managementsupport, commitment and capability; project team compo-sition, leadership and skill; business vision; organizationmaturity level; organization culture; internal/external projectteam communication, etc. All non-technical factors abovewe will used to build an ERP implementation methodologyas an activities for each phase.

Ø Preliminary of ERP Implementation MethodologyBased on the activities above, we can develop the ERPimplementation methodology as a preliminary design. Wecan divide five phases of the ERP implementation method-ology, such as:

Figure 2. Comparison of ERP Implementation Methodology

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(1) ERP Selection Phase, this phase will be comparing allof ERP product that will most suitable with the organiza-tion. It contains some activities, such as: aligning one ofERP product with an organization IS/IT strategy; aligningwith government / company policy; matching with an in-dustrial standard; business vision identification; suit-able with organizational culture, identify a budget of in-vestment, internal IS/IT (hardware and software) identifi-cation; ICT infrastructure identification, organization ma-turity level identification, identification of aligning betweenorganization business process with ERP business process.

(2) Project Preparation Phase contains some activitiessuch as: identification of top management support, com-mitment and capability; identification of project team com-position, leadership and skill; identification of projectscope, schedule, investment and role; function require-ment building; identification of internal/external projectteam communication; identification of legacy systems thatwill integrate with ERP product; choose of implementationstrategy; define a consultant skill; define a job descriptionof project team members; motivate of user involvement.

(3) Implementation & Development Phase contains someactivities, such as: developing implementation plan; ERPor software configuration; business process reengineering(BPR); data conversion; change management; system in-tegration; penetration application; and training.

(4) Operational and Maintenance Phase contains someactivities: operational and maintenance of software pack-age, evaluation and audit the system periodically.(5) Support and Monitoring Phase, ensuring sys-tem performance through ERP monitoring and support.

Aim of this study is proposing a new ERP implementationmethodology that can minimize a failure of implementationthis system. With this methodology, ERP implementationwill give an optimal return value for organization itself.This methodology has already involved some factors thatinfluenced an ERP implementation success. It give us aguidance to exercise some components that most impor-tant for implementation ERP system. That’s component,such as: how we know a top management support, com-mitment and capability; how we can build a project teamthat have a good composition, leadership and skill; howwe can identify the organization business vision, so it cansuitable with the ERP product that organization chosen;how we can exercise the project scope, schedule, invest-ment and role; how we can identify the organization matu-rity level, thus we can select the right ERP product andwhat modules we suppose to implement to support anoperational organization; how we can build a functional

requirement; how we can build a good communication ininternal/external project team, etc.

5. CONCLUSSION

In the light of the findings on this study, it can be con-cluded that ERP implementation methodology as prelimi-nary study divided into 5 phase, namely: (1) ERP SelectionPhase, (2) Project Preparation Phase, (3) Implementation &Development Phase, (4) Operational & Maintenance Phase,and (5) Support & Monitoring Phase. This methodologywill give the organization an optimal return value. Because,each phase contained some factors that influenced ERPimplementation success.

6. FURTHER RESEARCH

This study shown that some aspects influence ERP imple-mentation success, which we can classify into organiza-tion factor, technology factor and country / external orga-nization factor. Each aspect contains some activity thatshould be involved in ERP implementation methodologyas preliminary design that we proposed. For further re-search, we need to explore more deeply according to ERPimplementation methodology that suitable for organiza-tion culture especially in Indonesia and also fit to indus-trial sector.

REFERENCES

Ahituv Niv, Neumann Seev dan Zviran Moshe (2002), ASystem Development Methodology for ERP Systems, TheJournal of Computer Information Systems.

Allen D, Kern T, Havenhand M (2002), ERP Critical Suc-cess Factors: An Exploration of the Contextual Factors inPublic Sector Institution, Proceeding of the 35th HawaiiInternational Conference on System Sciences.

Al-Mashari M, Al-Mudimigh A, Zairi M (2003), EnterpriseResource Planning: A Taxonomy of Critical Factors, Euro-pean Journal of Operational Research.

Brown C., Vessey I. (1999), Managing the Next Wave ofEnterprise Systems: Leveraging Lessons from ERP, MISQuarterly Executive.

Dantes Gede Rasben (2006), ERP Implementation and

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Impact for Human & Organizational Cost), MagisterThesis of Information Technology, University of Indone-sia.

Davidson R. (2002), Cultural Complication of ERP, Com-munication of the ACM.Deloitte Consulting (1999). ERP’s Second Wave: Maximiz-ing the Value of Enterprise Applications and Processes,http://www.dc.com/Insights/research/cross_ind/erp_second_wave_global.asp

Esteves J, Pastor J. (2000), Toward Unification of CriticalSuccess Factors for ERP Implementation, Proceedings ofthe 10th Annual Business Information Technology (BIT)Conference, Manchester, UK.

Gargeya VB, Brady C. (2005), Success and Failure Factorsof Adopting SAP in ERP System Implementation, Busi-ness Process Management Journal.

Gunson, John dan de Blasis, Jean-Paul (2002), Implement-ing ERP in Multinational Companies: Their Effect on theOrganization and Individuals at Work, journal ICT.

Hasibuan Zainal A. and Dantes Gede Rasben (2009), TheRelationship of Organization Maturity Level and Enter 2009.

Management Journal.for ERP Implementation, IEEE Software.

Jying Information Systems Development Methodologiesand Approaches, Journal of Management InformationSystems.

Liang H, Xue Y, Boulton WR, Byrd TA (2004), Why West-ern Vendors don’t Dominate China’s ERP Market?, Com-munications of the ACM.

Martinsons MG (2004), ERP in China: One Package, TwoProfiles, Communication of the ACM.Mary Summer (2004), Enterprise Resource Planning, Up-

per Saddle River, New Jersey.

Motwani J, Akbul AY, Nidumolu V. (2005), Successful Imple-mentation of ERP Systems: A Case Study of an Interna-tional Automotive Manufacturer, International Journalof Automotive Technology and Management.

Murray MG, Coffin GWA (2001), Case Study Analysis ofFactors for Success in ERP System Implementation, Pro-ceeding of the Americas Conference on Information Sys-tems, Boston, Massachusetts.

O’Kane JF, Roeber M. (2004), ERP Implementation andCulture Influences: A Case Study, 2nd World Conferenceon POM, Cancun, Mexico.O’Leary E. Daniel (2000), Enterprise Resource PlanningSystem (Systems, Life Cycle, Electronic Commerce, andRisk), Cambridge University Press, Cambridge.

Parr A, Shanks G. (2000), A Model of ERP Project Imple-mentation, Journal of Information Technology.

Rajapakse and Seddon (2005), Utilizing Hofstede’s Dimen-sions of Culture, Investigated the Impact of National andOrganizational Culture on the adoption of Western-basedERP Software in Developing Country in Asia.

Reimers K. (2003), International Examples of Large-ScaleSystems – Theory and Practice I: Implementing ERP Sys-tems in China, Communication of the AIS.

Roseman M, Sedera W, Gable G (2001), Critical SuccessFactors of Process Modeling for Enterprise Systems, Pro-ceedings of the Americas Conference on Information Sys-tems, Boston, Massachusetts.

Siau K. (2001). ERP Implementation Methodologies — Past,Present, and Future, Proceedings of the 2001 InformationResources Management Association International Con-ference (IRMA’2001), Toronto, Canada.Soh C, Kien SS, Tay-Yap J. (2000), Enterprise Resource

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Planning: Cultural Fits and Misfits: Is ERP a UniversalSolution?, Communication of the ACM.

Somers TM, Nelson KG (2004), A Taxonomy of Playersand Activities Across the ERP Project Life Cycle, Informa-tion and Management.

Tsai W, Chien S, Hsu P, Leu J (2005), Identification of Criti-cal Failure Factors in the Implementation of EnterpriseResource Planning (ERP) System in Taiwan’s Industries,International Journal of Management and EnterpriseDevelopment.

Umble E, Haft R, Umble M. (2003), Enterprise ResourcePlanning: Implementation Procedures and Critical SuccessFactors, European Journal of Operational Research.

Wassenaar Arjen, Gregor Shirley dan Swagerman Dirk

(2002), ERP Implementation Management in Different Or-ganizational and Culture Setting, European AccountingInformation Systems Conference, http://accountingeducation.com/ecais

Xue, Y., et al. (2005), ERP Implementation Failure in ChinaCase Studies with Implications for ERP Vendors”, Interna-tional Journal Production Economics.

Zang, Z., Lee, M.K.O., Huang, P., Zhang, L., Huang, X.(2002), “A framework of ERP systems implementation suc-cess in China: An empirical study”, International JournalProduction Economics.

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I. IntroductionA cellular neural network (CNN) is a 2 dimensionalrectangular structure, composed by identical analogicalnon-linear processors, named cells [1]. CNN can be usedin many scientific applications, such as in signalprocessing, image processing and analyzing 3D complexsurfaces [9]. In this paper, we implement edge detectionprogram based on CNN and optimized using TEMPOprovided by CNN Simulator called CANDY Simulator [7].

The basic circuit unit of CNNs contains linear and nonlinearcircuit elements, which typically are linear capacitors, linearresistors, linear and nonlinear controlled sources, andindependent sources.

The structure of cellular neural networks is similar to thatfound in cellular automata; namely, any cell in a cellularneural network is connected only to its neighbor cells. Allthe cells of a CNN have the same circuit structure andelement values. Theoretically, we can define a cellular neuralnetwork of any dimension, but in this paper we will focusour attention on the two dimensional for image processing.A typical circuit of a single cell is shown in the figure 1below.

Paper

AbstractResult of edge detection using CNN could be not optimal, because the optimal result is based on template applied to theimages. During the first years after the introduction of the CNN, many templates were designed by cut and try techniques.Today, several methods are available for generating CNN templates or algorithms. In this paper, we presented a methodto make the optimal result of edge detection by using TEMPO (Template Optimization). Result shown that templateoptimization improves the image quality of the edges and noise are reduced. Simulation for edge detection uses CANDYSimulator, then we implementing the program and optimized template using MATLAB. Comparing to Canny and Sobeloperators, image shapes result from CNN edge detector also show more realistic and effective to user.

Keywords: CNN, edge detection, TEMPO, Template optimization.

Saturday, August 8, 200913:30 - 13:50 Room L-211

EDGE DECTION USING CELLULAR NEURAL NETWORKAND TEMPLATE OPTIMIZATION

Figure 1. Typical circuit of a single cell

Each cell contains one independent voltage source Euij (Input), one independent current source I (Bias),several voltage controlled current sources Inuij, Inyij,and one voltage controlled voltage source Eyij (Output).The controlled current sources Inuij are coupled toneighbor cells via the control input voltage of eachneighbor cell. Similarly, the controlled current sourcesInyij are coupled to their neighbor cells via the feedbackfrom the output voltage of each neighbor cell [2].

Widodo Budiharto, Djoko Purwanto, Mauridhi Hery Purnomo

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Electrical Engineering Department Institue Technology SurabayaJl. Raya ITS, Sukolilo, Surabaya 60111, Indonesia

[email protected]

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The CNN allows fully parallel image processing, a givenprocessing being executed simultaneously for the entireimage. An example of 2 dimensional cellular neuralnetworks is shown in Fig 2:

Figure 2. A two-dimensional cellular neural network.

This circuit size is 4x4. The squares are the circuit unitscalled cells. The links between the cells indicate that thereare interactions between the linked cells [2].

The state equation of a cell is [2]:

(1)Where represent the state, ukl is the input, ykl is theoutput and is the threshold of the cell C(i,j). A(i,j;k,l) isthe feedback operator and B (i,j;k,l) is the input synapticoperator. The ensemble (A, B, z) is named template. are the cells from a r-order neighborhood Sr of the cell(i,j).

(2)

Figure 3. Signal flow structure of a CNN with a 3x3neighborhood.

The system structure of a center cell is represented inFigure 4:

Figure 4. Structure for cell Cij, arrow printed in bold markparallel data path from the input and the output of thesurround cell ukl and ykl. Arrows with thinner lines denotethe threshold, input, state and output, z, uij, xij and yijrespectively.

II. LITERATURESEdge DetectionIn general, edge detection defines as boundary between2 region ( two adjacent pixel) that have very high differentintensity [3]. Some of the others operator are Sobel,Prewitt, Roberts and Canny [3]. In this research we willcompare the result with Sobel and Canny edge detectoras another important methods [6].

EDGEGRAY CNNWe use EDGEGRAY CNN for edge detection gray scaleinput images that accepting gray-scale input images andalways converging to a binary output image . Oneapplication of this CNN template is to convert gray –scaleimages into binary images, which can then be used asinputs to many image-processing tasks which require abinary input image. Here is gray scale edge detectiontemplate with z/bias used are -0.5:

Table 1 : Template for gray scale edge detection

III. SYSTEM ARCHITECTURE

We use MATLAB and webcam for capturing images, andCANDY (CNN Simulator) [7] for testing the color imagesedge detection. For optimizing template, we use TEMPOprovided by CANDY. Diagram block that show the orderof the process of the system shown in figure below :

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Figure 5. Diagram block of Edge Detection using CNNusing template optimization

First, the original edgegray template given to TEMPOprogram. Using some features of its program, we canoptimizing the template. As the result, template belowshow optimized template for edgegray edge detection : :

neighborhood: 1feedback: 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000control:-1.0000 -1.0000 -1.0000-1.0000 8.0000 -1.0000-1.0000 -1.0000 -1.0000current: -1.0000

Then, the script below show program generated byTEMPO to optimize the edgegray detection using CNNby modifying Iteration.

{START: temrun}Initialize SIMCNN CSD

TimeStep 0.100000IterNum 80OutputSampling 1

Boundary ZEROFLUXSendTo INPUTPicFill STATE 0.0

TemplatePath C:\Candy\temlib\CommunicationPath C:\CANDY\TemLoad o_edgegray.temRunTem

Terminate{STOP: temrun}

The template value and script above will be used byCANDY for simulation.

IV. RESULTSIn this section the experimental results obtained byCANDY and MATLAB are presented. Let us consider animage figure 6,

Figure 6. Edge detection using CANDY Simulator

Figure above is the result of edge detection withouttemplate optimization, it shown that many noises arised(see image detail below).

Figure 7. Result of edge detection without templateoptimization

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Using template and script above, we try to implement edgedetection based on CNN using CANDY, the result shownbelow:

Figure 8. Result of edge detection with templateoptimization

Figure above shows that using template optimization,some noises are reduced. This indicated that templateoptimization succesfully implemented. Figure below is aprogram developed by MATLAB to implementing theedge detection using CNN and template optimization.

Figure 9. Implementation using MATLAB for templateoptimization.

To evaluate the effectiveness of CNN to any operators,we Compared to Sobel and Canny operator, from thefigure below, indicated that image processed using

CNN edge detector show more realistic and easy tounderstand.

Figure 10. Original image (a), Comparing to Sobel (c) andCanny edge detector (d), CNN edge detector with z=0.8optimized with closing operation (b) show more realisticto user.

V. CONCLUSIONIn this paper, we have investigated the implementation ofCNN and template optimization for edge detection. Basedon the experiment, template optimization proved able toimproves the quality of images for edge detection.Template optimization also reduced noises, but it makessome important lines disconnected. To solve this problem,we can use closing operation.

VI. FUTURE WORK

CNN very important method for image processing. Wepropose this system can be used for system that needhigh speed image processing such as robotics system fortracking object and image processing in medicalapplication. We will continue working on CNN fordevelopment of high speed image tracking in servantrobot.

VII. REFERENCES[1]. Chua LO, Yang L, “Cellular Neural Networks:

Theory”, IEEE Transactions on Circuit and System,vol 35, 1998, pp.1257-72.

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[2] Chua LO, Roska T, Cellular Neural Networks andVisual Computing, Cambridge University Press,2002.

[3] Gonzales, Rafael C. and Richard E. Woods. DigitalImage Processing. 3rd ed. Englewood Cliffs, NJ:Prentice-Hall, 2004.

[4] Alper Basturk and Enis Gunay, “Efficient edgedetection in digital images using a cellular neuralnetwork optimized by differential evolutionalgorithm”, Expert System with Applciation 35,2009, pp 2645-2650.

[5] Koki Nishizono and Yoshifumi Nishio, “ImageProcessing of Gray Scale Images by Fuzzy CellularNeural Network”, International workshop onNonlinear Circuits and Signal Processing, Hawaii,USA, 2006.

[6] Febriani, Lusiana, “Analisis Penelusuran Tepi Citramenggunakan detector tepi Sobel dan Canny”,Proceeding National Seminar on Computer andIntelligent System, University of Gunadarma, 2008.

[7] CANDY Simulator,www.cnntechnology.itk.ppke.hu\

[8]. CadetWin CNN applicaton development \environment and toolkit under Windows.Version3.0, Analogical and Neural Computing Laboratory,Hungarian Academy of Sciences, Budapest, 1999.

[9] Yoshida, T. Kawata, J. Tada, T. Ushida, A.Morimoto, Edge Detection Method with CNN, SICE2004 Annual Conference, 2004, pp.1721-1724.

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Paper

AbstractAuthentication is applied in the system for maintaining the confidentiality of information and data security. How commonis through the use of a password. However, the authentication process such as this would cause inconvenience to bothusers and administrators, that is, when taken in the environment that has many different systems, where on each system toimplement the authentication process is different from one another. Through the method of global password, a user doesnot need to enter passwords repeatedly to enter into multiple systems at once. In addition, the administrator does notneed to adjust the data in each database system when there are changes occur on data user. In this article, identified theproblem that faced e-learning institution in terms of authentication using the password on a web-based informationsystem, defined 7 characteristics of the concept of authentication using the global password method as a step of problem-solving, and set benefit from the implementation of the new concept. In addition, also shown the snippets of programwritten using ASP script and its implementation in e-learning the Students Information Services (SIS) Online JRS atPerguruan Tinggi Raharja. Global password as a method in e-learning, not only the security level which is the attention,but also the convenience and ease of use both in the process and at the time control.

Index Terms—Global Password, Authentication, Security, Database, Information System

Saturday, August 8, 200914:45 - 15:05 Room L-212

Global Passwordfor Ease of Use, Control and Security in Elearning

I. IntroductionIn a system, external environment (environments) affectssystem operation, and can be harmful or beneficial to thesystem. External security, internal security, security of theuser interface are three types of security systems that canbe used [3]. Security in a system become very importantbecause the information system provide informationneeded by the organization [2]. Aspect of security that isoften observed is in the case of the user interface, that isrelated to the identification of the user before the user ispermitted to access the programs and data stored. One ofthe main component is the authentication. Type of au-thentication that is most widely used is knowledge-basedauthentication, that is, through the use of password orPIN [1].

II. ProblemsIn the information system that implements authenticationusing passsword, each user logs in to the system by typ-ing in the username and password, which ideally is onlyknown by the system and the user.

Figure 1. Users log in to perform in a system

The process above does not seem to have have any prob-lems. This is because the user only access to one systemonly. However, different conditions will be felt if the user ison the environment where there are more than one system.

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Untung Rahardja, M.T.I.STMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

Jazi Eko Istiyanto, Ph.DGajah Mada University

[email protected]

Valent Setiatmi, S.KomSTMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

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When each system has its own authentication process, itcan cause inconvenience for the user who has a lot ofaccounts. This can make it, because every time a user ac-cess to different systems, then he must type the passwordone by one for each system. The situation will becomemore difficult if the user has a username and password thatis different for each system.

Figure 2. Users log in to perform in more than onesystem

The logging in process is a time where the system con-vinced that users who are trying to access is actually en-titled. Web-based information systems usually store datain regard to a username and password on a table in thedatabase. Therefore, the system will check into the data-base whether the username and password entered is inaccordance or not.

Figure 3. A system to check the username and password inthe user database

In the management information system, usually there areadministrators who are responsible with regard to authen-tication. Administrator must be able to ensure that the au-thentication data for each user on the system always up-dated. When there are changes in user and password, ad-ministrators must be ready to update data in a databaserelated to the system.This kind of condition will be complicated for an environ-ment within multiple systems, that is when each system

has its own database password. The difficulty lies in thesynchronization of data authentication user between onesystem to other systems. Especially when there is a userwho has account on more than one system.

Figure 4. Each system to check the username andpassword in the user database of each

In this case, if a user intends to change the password ofthe entire account, the administrator must be ready to up-date the data with regard to the user’s password on eachdatabase system. The administrators must also be able toknow in which system does the user has an account in it.Circumstances such as this would complicate the adminis-trators in efforts to ensure that the data of user authentica-tion is always updated.

III. Literature Review

1. Research conducted by Markus Volkmer of HamburgUniversity of Technology Institute for Computer Technology Hamburg, Germany, entitled Entity Authentication and Key Exchange authenticated with parity TreeMachines (TPMS). This research is a concept proposedas an alternative to secure the Symmetric Key. Addingthe direct methods to access to many systems usingTPMS. TPMS can avoid using a Man-In-The-Middleattack.

2. Research conducted by Shirley Gaw and Edward W.Felten titled Password Management Strategies forOnline Accounts. This study discusses the securitypassword that has been developed into a commentaryto implement the password management strategies thatare focused on the account online. There is a gap between what technology can do to help and now havebeen provided such a method. The method is feasiblewith the current log to avoid identity theft and demonstrate the user not to use the book to the web sites.

3. Research conducted by Whitfield Diffie titled Authentication and authenticated key exchanges discuss thetwo parts to add a password as the exchange point byusing a simple technique asymmetric. Goal one proto

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col to communicate over a system with the assurancethat a high level of security. Password security is fundamental that the absence of any point of the exchangeare interconnected. Authentication and key exchangesmust be related, because it can enable someone to takeover a party in the key exchange.

4. Research conducted by Pierangela Samarati (ComputerScience Technology) sushil & Jajodia (Center for Secure Information System), entitled Data Security. Thedevelopment of computer technology is increasinglyfast, sophisticated and have high capability include:the memory capacity of the larger, the process datamore quickly and the function of a very complex (multifunction) and more easily operated through a series ofcomputer program packages, the process also affectsthe security of data. From the results of the referencereference from some of the research and literature, thereare some steps that can be done as a form of datasecurity, including, namely: Identification and Authentication, Access Control, Audit, Encryption. The stepstaken to maintain the Confidentiality / Privacy, Integrity and Availability (availability) of data. Implementation of the research can be seen in some instances theprocess of securing data for an application. Researchwill also inform about the introduction of various instruments of security data. Example: cryptographytechniques as the security password. In accordancewith the progress of information technology at thistime, research should also be how to manage the security of data on the Internet.

5. Research conducted by Chang-Tsun Li, entitled Digital Watermarking for Multimedia Authenticationschemes from the Department of Computer Science University of Warwick Coventry CV4 7AL UK. This studydiscusses about the Digital Watermarking schemes forMultimedia. Multimedia can be the strength of the digital processing device for the duplication and manipulation can improve the perfect forgery and disguise.This is a major concern in the current era of globalization. The importance of validation and verification thecontents become more evident and acute. With thetraditional digital signature information that is less appropriate because it may cause the occurrence of counterfeiting. Approach to validation data in digital media.Data validation techniques for digital media is dividedinto two categories, namely providing technical information and basic techniques watermark-base. The maindifference from the two categories of these techniquess that in the endorsement to provide basic information,and data authentication or signature.

6. Research conducted by T. Mark A. Lomas and Roger

Needham, entitled Strengthening Passwords. This research discusses a method to strengthen the password / password. This method does not require theuser to memorize or record the old password, and donot need to take the hardware. Traditional password isstill the most common basis for the validation, the users often have a weak password, strong password because it is difficult to remember. Password strengthenthe expansion of traditional password mechanisms.Techniques of this method is easy to implement in thesoftware and is conceptually simple.

7. Research conducted by Benny Pinkas and TomasSander, entitled Securing Passwords Against Dictionary Attacks. This study discusses the use of a password is a main point of the sensitivities in securitysensitive. From the perspective can help to solve thisproblem by providing the necessary restrictions in thereal world, such as infrastructure, hardware and software available. It is easy to implement and overcomesome of the many difficulties the method previouslyproposed, from improving the security of this authentication scheme. Proposed scheme also provides better protection against attacks from the service user account.

8. Research conducted by Kwon Taekyoung titled Authentication and Key Agreement via Memorable Password discuss about a new protocol called the Agreement Memorable Password (AMP). AMP is designedto strengthen password and sensitive to the dictionary attack. AMP can be used to improve security inthe distributed environment. Most of the methods usedwidely in touch with some of the benefits such as simplicity, comfort, ability to adapt, mobility, and fewerhardware requirements. That requires the user to remember only one with a password. Therefore, thismethod allows the user to move comfortably without asign of hardware.

IV. Problem Solving

To overcome the problems as described above, can bedone through the application of the methods of the GlobalPassword. Here are 6 characteristics of the Global Pass-word applied to the authentication process in the informa-tion system:1. Each user has only one username and password2. Username and password for each user in each system

is the same3. Users just log in once to be able to enter more than

one system4. User authentication data for the entire system is stored

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in the same database5. There is levels of authorization6. Adjustment user session on each system7. The data of users’s password stored in the database

have been encrypted

Problem in terms of user inconvenience in typing theusername and password repeatedly solved with the sim-plification process of authentication. Based on point 3 ofthe characteristic of the Global Password, a user only needto do that once, that is at the time of the user first logs in tothe system.

After the user do the log in process, and declared eligible,then the user can go directly to some of the desired sys-tem without having to type the username and passwordagain. With notes that the user has an account on thesystems that will be accessed.

Figure 5. User login beginning only one time

This may be related to two other characteristics of theGlobal Password, i.e. point 1 and point 2. For one user,only given a single username and password, which can beused for the entire system at once. The existence of thesame username and password is what allows for the com-munication between the system in terms of data verifica-tion. To make user can move from one system to anothersystem without logging in, then each system must also beable to read session one another and to adjust them on it.This condition is in accordance with the characteristic ofthe Global Password, that is point 6.

To make it easier for administrator to control the data ofuser authentication, conducted with storing data of theusername and password at the same place. In accordancewith the Global Password characteristic points on the num-ber 4 (four), the data is stored in a table in a single data-base that is used collectively by entire related system.

Figure 6. Data user username and password for eachsystem stored in a database the same

This condition will be easier for administrators to controlin the user authentication data, because he does not needto update at some database systems caused by a changehappened at a single user.

In addition, at the point 5 of characteristics of the GlobalPassword, is mentioned that this method is also pplied tosupport user-level authorization. In the table storing theusername and password, authorization level can be distin-guished from each user based on the classification of datastored, in accordance with the desires and needs of theorganization.

In terms of security, the Global Password is also equippedby the encryption process. Characteristics according tothe Global Password point number 7 (seven) said that pass-word for each user is stored in the form of encryption, soit’s not easy to know the real person’s password by an-other.

V. Implementation

Authentication method to use the Global Password is imple-mented in Raharja University, namely the information sys-tem SIS OJRS (Online JRS). Students Information Services,or commonly abbreviated SIS, is a system developed byUniversity Raharja for the purpose of information servicessystem as student optimal [4]. Development of SIS is alsoan access to the publication Raharja Universities in thefield of computer science and IT world in particular [4].SIS has been developed in several versions, each of whichis a continuation from the previous version of SIS. SISOJRS (Online Schedule Study Plan) is a version of the SIS-4. Appropriate name, SIS OJRS made for the needs of thestudent lecture, which is to prepare the JRS (ScheduleStudy Plan) and KRS (Card Plan Studies) students.In the SIS OJRS there are other subsystem-subsystem,

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RPU ADM, ADM Lecturer, Academic, GO, Pool Registra-tion, Assignment, and Data Mining. Each subsystem isassociated with one or more of the Universities in Raharja.Therefore, to facilitate the user in the access switch orapply the concept of inter-subsystem Global Password.For, not uncommon in the user account have more thanone subsystem, and must move from one subsystem toanother subsystem.

Figure 7. Log in page for the initial authentication at theSIS OJRS

Picture above is a display screen when the user first willenter and access the SIS OJRS. On that page, the usermust type the username and password for authentication.The system will then check the authentication data. Whendeclared valid, the user can directly access the subsystem-subsystem which is in the SIS OJRS without a need totype the username and password again, of course with theappropriate level of authorization given to the user con-cerned.

A. DatabaseSIS OJRS implemented on the University Raharja data-base using SQL Server. In the database server, in additionto database-the database used by the system, also pro-vided as a special master database to store all the data theuser username and password. Database is integrated withall other systems, including versions of SIS previous.This database is created on the table-a table that is re-quired in connection with the authentication process.There are two types of tables that must be prepared,namely: a table that contains data authentication, and au-thorization level information table.

Figure 9. Table structure Tbl_Password

The table above is a table which is the main place wheredata needed for authentication. These fields are requiredin compliance with the existing system. Field Name,Username, Password, Occupation, and IP_Address is afield that describes the user data itself. While the field-work as a field the next time the user authorization levelentrance to each subsystem.

Fill in the password field should be in the form that hasbeen encrypted. This applies not so easy to guess pass-word by another person, in considering this method canbe used one password for entry to many systems at once.The form of encryption that can be referred to the various,customized to the needs of the organization. Can only benumber only, or combination of numbers, letters, and othercharacters.

Specific to the field as OJRS_All, OJRS_RPU,OJRS_ADM_Dosen and so forth, is made with the datatype smallint. This is because the contents of the field-field is only a number. Value for each digit represents thelevel of authorization granted to the user concerned.

Figure 10. Tbl_Password table of contents

To explain the value of the number, then needed anothertable-table, which functions as a description for each fieldin the main table.

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Figure 11. Table structure Tbl_OJRS_RPU

Fill in the table, the table explains the meaning of eachnumber is entered in the main table, that is, the user autho-rization level. Whether the user can only read (read) sys-tem, make changes to data stored (update), or have norights at all (null).

Figure 12. Tbl_OJRS_RPU table of contents

B. Listing ProgramBy using the Global Password, the verification processthrough the input username and password only once. Tofurther maintain the security system, the password is en-tered first will be encrypted. Encryption methods are notlimited, tailored to the needs. In addition, the inspection IPAddress can also be added at the time of verification. Hereis a snippet of ASP scripts that are used at the time theuser logs in.

Figure 13. Snippets of the script when the user logs in

If after the user logs in and declared eligible for entry intothe system, then a session will be formed. This sessionenter into other systems or not.

Here is a snippet of ASP scripts that are used when a userhas successfully logged on SIS ago OJRS want to access

another system is in, in this case is the ADM RPU:

In the script this is the level of user authorization review. Ifthe user does not have rights to the system, the automaticuser can not enter into it. Laying down the script into theappropriate key in the security system. In this case, theidentity proofing and user-level authorization must be inthe top of the, or each time a user wanted to enter in eachsystem.

VI. Conclusion

Authentication is an important part in the security system.Will also be optimal when considering the environmentand the needs of both users and administrators. GlobalPassword is a new concept that will accommodate the needsof user convenience in accessing information systems,especially on the environmental condition of a compoundsystem. From an administrator, will also become easier inthe case of authentication data for each system. In addi-tion, the Global Password still maintaining the confidenti-ality of the data in the system for the early goal, security ofthe information system.

References

[1]Chandra Adhi W (2009). Identification and Authentica-tion: Technology and Implementation Issues.Ringkasan Makalah. Diakses pada 4 Mei 2009 dari:

http://bebas.vlsm.org/v06/Kuliah/Seminar-MIS/ 2008/254/254-08-Identification_and_Authentication. pdf

[2]Jogiyanto Hartono (2000). Pengenalan Komputer: DasarIlmu Komputer, Pemrograman, Sistem Informasi danIntelegensi Buatan. Edisi ketiga. Yogyakarta: Andi.

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[3]Missa Lamsani (2009). Sistem Operasi Komputer:Keamanan Sistem. Diakses pada 5 Mei 2009 dari:http://missa.staff.gunadarma.ac.id/Downloads/files/6758/BAB8.pdf

[4]Untung Rahardja (2007). Pengembangan Students In-formation Services di Lingkungan Perguruan TinggiRaharja. Laporan Pertanggung Jawaban. Tangerang:Perguruan Tinggi Raharja.

[5]Untung Rahardja, Henderi, dan Djoko Soetarno (2007).SIS: Otomatisasi Pelayanan Akademik KepadaMahasiswa Studi Kasus di Perguruan Tinggi Raharja.Jurnal Cyber Raharja. Edisi 7 Th IV/April. Tangerang:Perguruan Tinggi Raharja.

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MINING QUERIES FASTER USING MINIMUMDESCRIPTION LENGTH PRINCIPLE

Department of Computing Science Universiteit [email protected]

Information Technology Department – Faculty of Computer Study STMIK RaharjaJl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

email: [email protected]

AbstractEver since the seminal paper by Imielinski and Mannila [8], inductive databases have been a constant theme in the datamining literature. Operationally, an inductive database is a database in which models and patterns are _rst classcitizens.Having models and patterns in the database raises many interesting problems. One, which has received little attention sofar, is the following: do the models and patterns that are stored help in computing new models and patterns? For example,if we have induced a classi_er C from the database and we compute a query Q. Does knowing C speed up the inductionof a new classi_er on the result of Q? In this paper we answer this problem positively for one speci_c class of models, viz.,the code tables induced by our Krimp algorithm. The Krimp algorithm was built using minimum description length(MDL) principle. In Krimp algorithm, if we have the code tables for all tables in the database, then we can approximatethe code table induced by Krimp on the result of a query, using only these global code tables as candidates; that is, we donot have to mine for frequent item sets one the query result. Since Krimp is linear in the number of candidates and Krimpreduces the set of frequent item sets by many orders of magnitude, this means that we can speed up the induction of codetables on query results by many orders of magnitude.

Keywords: Inductive Database, Frequent Item Sets, MDL

1. IntroductionEver since the start of research in data mining, it has beenclear that data mining, and more general the KDD process,should be merged into DBMSs. Since the seminal paperby Imielinski and Mannila [8], the so-called inductive da-tabases have been a constant theme in data mining re-search.

Perhaps surprisingly, there is no formal de_nition of whatan inductive database actually is. In fact, de Raedt in [12]states that it might be too early for such a de_nition. Thereis, however, concensus on some aspects of inductive da-tabases. An important one is that models and patternsshould be _rst class citizens in such a database. That is,e.g., one should be able to query for patterns.

Having models and patterns in the database raises inter-esting new problems. One, which has received little atten-tion so far, is the following: do the models and patternsthat are stored help in computing new models and pat-terns? For example, if we have induced a classi_er C fromthe database and we compute a query Q. Does knowing Cspeed up the induction of a new classi_er on the result ofQ?In fact, this general question is not only interesting in thecontext of inductive databases, it is of prime interest ineveryday data mining practice.

In the data mining literature, the usual assumption is thatwe are given some database that has to be mined.

Diyah Puspitaningrum, Henderi

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In practice, however, this assumption is usually not met.Rather,the construction of the mining database is oftenone of the hardest parts of the KDD process. The dataoften resides in a data warehouse or in multiple databases,and the mining database is constructed from these under-lying databases.From most perspectives, it is not very interesting to knowdatabase are of no importance whatsoever.It makes a di_erence, however, if the underlying databasestabases, one would hope that knowing such models wouldhelp in modelling the specially constructed ‘mining data-base. For example, if we have constructed a classi_er on adatabase of customers, one would hope that this wouldhelp in developing a classi_er for the female customersonly.

In this paper we study this problem for one speci_c classof models, viz., the code tables induced by our Krimp algo-rithm [13]. Given all frequent item sets on a table, Krimpselects a small subset of these frequent item sets. Thereason why we focus on Krimp is that together the se-lected item sets describe the underlying data distributionof the complete database very well, see, e.g., [14, 16].More in particular. we show that if we know the code tablesfor all tables in the database, then we can approximate thecode table induced by Krimp on the result of a query, us-ing only the item sets in these global code tables as candi-dates.

Since Krimp is linear in the number of candidates and Krimpreduces the set of frequent item sets by many orders ofmagnitude, this means that we can now speed up the in-duction of code tables on query results by many orders ofmagnitude.This speed-up results in a slightly less optimal code table,but it approximates the optimal solution within a few per-cent. We’ll formalise \approximation” in terms of MDL [7].Hence, the data miner has a choice: either a quick, goodapproximation, or the optimal result taking longer time tocompute.

The road map of this paper is as follows. In the next Sec-tion we formally state the general problem. Next, in Section3 we give a brief introduction to our Krimp algorithm. InSection 4, we restate the general problem in terms of Krimp.Then in Section 5 the experimental set-up is discussed.Section 6 gives the experimental results, while in Section 7these results are discussed.Section 8 gives an overview of related research. The con-clusions and directions for further research are given inSection 9.

2. Problem StatementThis section starts with some preliminaries and assump-tions. Then we introduce the problem informally. To

formalise it we use MDL, which is briey discussed.

2.1 Preliminaries and Assumptions We assume that ourdata resides in relational databases. In fact, note thatthe union of two relational databases is, again, arelational database. Hence, we assume, without loss ofgenerality, that our data resides in one relationaldatabase DB. So, the mining database is constructedfrom DB using queries. Given the compositionality ofrelational query languages, we may assume, again without loss of generality, that the analysis database isconstructed using one query Q. That is, the analysisdatabase is Q(DB), for some relational algebraexpression Q. Since DB is _xed, we will often simplywrite Q for Q(DB); that is we will use Q to denote boththe query and its result.

2.2 The Problem Informally In the introduction we statedthat knowing a model on DB should help in inducing amodel on Q. To make this more precise, let A be ourdata mining algorithm. A can be any algorithm, it may,e.g., compute a decision tree, all frequent item sets or aneural network. LetMDB denote the model induced byA from DB, i.e, MDB = A(DB). Similarly, let MQ = A(Q).We want to transform A into an algorithm A_ that takesat least two inputs, i.e, both Q and MDB, such that:

1. A_ gives a reasonable approximation of A when applied to Q, i.e., A_(Q;MDB) tMQ

2. A_(Q;MDB) is simpler to compute than MQ.The second criterion is easy to formalise: the runtimeof A_ should be shorter than that of A. The _rst one isharder. What do we mean that one model is anapproximation of another? Moreover, what does it meanthat it is a reasonable approximation? There are manyways to formalise this. For example, for predictivemodels, one could use the di_erence betweenpredictions as a way to measure how well one modelapproximates. While for clustering, one could use thenumber of pairs of points that end up in the samecluster.

We use the minimum description length (MDL)principle [7] to formalise the notion of approximation.MDL is quickly becoming a popular formalism in datamining research, see, e.g., [5] for an overview of otherapplications of MDL.

2.3 Minimum Description Length MDL like its close cousinMML (minimum message length) [17], is a practicalversion of Kolmogorov Complexity [11]. All threeembrace the slogan Induction by Compression.For MDL, this principle can be roughly described asfollows.

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Given a set of models1 H, the best model H 2 H is theone that minimizes L(H) + L(DjH) in which L(H) is thelength, in bits, of the description of H, and_ L(DjH) is the length, in bits, of the description of thedata when encoded with H.One can paraphrase this by: the smaller L(H) + L(DjH),the better H models D.

What we are interested in is comparing two al-gorithmson the same data set, viz., on Q(DB).Slightly abusing notation, we will write L(A(Q)) forL(A(Q)) + L(Q(DB)jA(Q)), similarly, we will writeL(A_(Q;MDB)). Then, we are interested in comparing1MDL-theorists tend to talk about hypothesis in thiscontext, hence the H; see [7] for the details.L(A_(Q;MDB)) to L(A(Q)). The closer the former is tothe latter, the better the approximation is.Just taking the di_erence of the two, however, can bequite misleading. Take, e.g., two databases db1 anddb2 sampled from the same underlying distribution,such that db1 is far bigger than db2. Moreover, _x amodel H. Then necessarily L(db1jH) is bigger thanL(db2jH).

In other words, big absolute numbers do notnecessar-ily mean very much. We have to normalisethe di_er-ence to get a feeling for how good the approximation is. Therefore we de_ne the asymmetricdissimilarity mea-sure (ADM) as follows.Definition 2.1. Let H1 and H2 be two models for adataset D. The asymmetric dissimilarity measureADM(H1;H2) is de_ned by:ADM(H1;H2) = jL(H1) _ L(H2)j L(H2) Note that thisdissimilarity measure is related to the Normalised Compression Distance. The reason why we use thisasymmetric version is that we have a \gold standard”.We want to know how far our approximate resultA_(Q;MDB) deviates from the optimal result A(Q).

2.4 The Problem Before we can formalise our prob-lemusing the notation introduced above, we have one morequestion to answer: what is a reasonableapprox-imation? For a large part the answer to thisquestionis, of course, dependent on the application inmind. An ADM in the order of 10% might be perfectlyalright in one application, while it is unacceptable inanother.Hence, rather than giving an absolute number, we makeit into a parameter _. Problem:For a given data mining algorithm A, devise analgo-rithm A_, such that for all relational algebraexpressions Q on a database DB:

1. ADM(A_(Q;MDB);A(Q)) _ _

2. Computing A_(Q;MDB) is faster than computing A(Q)

2.5 A Concrete Instance: Krimp The ultimate solution tothe problem as stated in above would be an algorithmthat transforms any data mining algorithm A in analgorithm A_ with the requested properties. This is arather ambitious, ill-de_ned (what is the class of alldata mining algo-rithms?), and, probably, not attainable goal. Hence, in this paper we take a more modestapproach: we trans-form one algorithm only, our Krimpalgorithm.The reason for using Krimp as our problem instance isthreefold. Firstly, from earlier research we know thatKrimp characterises the underlying data distributionrather well; see, e.g., [14, 16]. Secondly, from earlierresearch on Krimp in a multi-relational setting, we already know that Krimp is easily transformed for joins[10]. Finally, Krimp is MDL based. So, notions such asL(A(Q)) are already de_ned for Krimp.

3. Introducing Krimp For the convenience of the readerwe provide a brief introduction to Krimp in thissection, it was originally introduced in [13] (althoughnot by that name) and the reader is referred to thatpaper for more details.Since Krimp is selects a small set of representative itemsets from the set of all frequent item sets, we _rst recallthe basic notions of frequent item set mining [1].

3.1 Preliminaries Let I = fI1; : : : ; Ing be a set of binary (0/1valued) attributes. That is, the domain Di of item Ii isf0; 1g. A transaction (or tuple) over I is an element ofQi2f1;:::;ng Di. A database DB over I is a bag of tuplesover I. This bag is indexed in the sensethat we can talk about the i-th transaction.An item set J is, as usual, a subset of I, i.e., J _ I. Theitem set J occurs in a transaction t 2 DB if 8I 2 J : _I (t)= 1. The support of item set J in database DB is thenumber of transactions in DB in which J occurs.That is, suppDB(J) = jft 2 DBj J occurs in tgj. An itemset is called frequent if its support is larger than someuser-de_ned threshold called the minimal support ormin-sup. Given the A Priori property, 8I; J 2 P(I) : I _ J !suppDB(J) _ suppDB(I) frequent item sets can be minede_ciently level wise, see [1] for more details.Note that while we restrict ourself to binary databasesin the description of our problem and algo-rithms, thereis a trivial generalisation to categorical databases. Inthe experiments, we use such categorical databases.

3.2 Krimp The key idea of the Krimp algorithm is the codetable. A code table is a two-column table that has itemsets on the left-hand side and a code for each item seton its right-hand side. The item sets in the code table

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are ordered descending on 1) item set length and 2)support size and 3) lexicographically. The actualcodes on the right-hand side are of no importance:their lengths are. To explain how these lengths arecomputed the coding algorithm needs to be introduced.A transaction t is encoded by Krimp by searching forthe _rst item set c in the code table for which c _ t.The code for c becomes part of the encoding of t. If tn c 6= ;, the algorithm continues to encode t n c.Since it is insisted that each code table contains atleast all singleton item sets, this algorithm gives aunique encoding to each (possible) transactionover I.The set of item sets used to encode a transaction iscalled its cover. Note that the coding algorithmimplies that a cover consists of non-overlapping itemsets.The length of the code of an item in a code table CTdepends on the database we want to compress;the more often a code is used, the shorter it shouldbe. To compute this code length, we encode eachtransaction in the database DB. The frequency of anitem set c 2 CT, denoted by freq(c) is the number oftransactions t 2 DB which have c in their cover Thatis, freq(c) = jft 2 DBjc 2 cover(t)gj The relative frequency of c 2 CT is the probability that c is used toencode an arbitrary t 2 DB, i.e.P(c) = freq(c) Pd2CT freq(d)For optimal compression of DB, the higher P(c), theshorter its code should be. Given that we also needa pre_x code for unambiguous decoding, we use thewell- known optimal Shannon code [4]:lCT (c) = _log(P(cjDB)) = _log_ freq(c) Pd2CTfreq(d)_The length of the encoding of a transactionis now simply the sum of the code lengths of the itemsets in its cover. Therefore the encoded size of atransaction t 2 DB compressed using a speci_ed codetable CT is calculated as follows:LCT (t) = X c2cover(t;CT) lCT (c)The size of the encoded database is the sum of thesizes of the encoded transactions, but can also becomputed from the frequencies of each of the elements in the code table:LCT (DB) = Xt2DB LCT (t)= _Xc2CT freq(c) log_ freq(c) Pd2CT freq(d)_To _nd the optimal code table using MDL, we needto take into account both the compressed databasesize, Figure 1: Krimp in action as described above, aswell as the size of the code table.For the size of the code table, we only count thoseitem sets that have a non-zero frequency.

The size of the right-hand side column is obvious; it issimply the sum of all the di_erent code lengths. For thesize of the left-hand side column, note that the simplestvalid code table consists only of the singleton item sets.This is the standard encoding (st), of which we use thecodes to compute the size of the item sets in the left-handside column. Hence, the size of code table CT is given by:L(CT) = X c2CT:freq(c)6=0 lst(c) + lCT (c) In [13] we de_nedthe optimal set of (frequent) item sets as that one whoseassociated code table minimises the total compressed size:L(CT) + LCT (DB)

Krimp starts with a valid code table (only the collection ofsingletons) and a sorted list of candidates (frequent itemsets). These candidates are assumed to be sorted descend-ing on 1) support size, 2) item set length and 3) lexico-graphically. Each candidate item set is considered by in-serting it at the right position in CT and calculating thenew total compressed size. A candidate is only kept in thecode table i_ the resulting total size is smaller than it wasbefore adding the candidate. If it is kept, all other elementsof CT are reconsidered to see if they still positively con-tribute to compression. The whole process is illustrated inFigure 1. For more details see [13].

4 The Hypothesis for Krimp If we assume a _xed minimumsupport threshold for a database, Krimp has only one es-sential parameter:the database. For, given the database and the (_xed) mini-mum support threshold, the candidate list is also speci_ed.Hence, we will simply write CTDB and Krimp(DB), to de-note the code table induced by Krimp from DB. SimilarlyCTQ and Krimp(Q) denotethe code table induced by Krimp from the result of apply-ing query Q to DB.Given that Krimp results in a code table, there is only onesensible way in which Krimp(DB) can be re-used to com-pute Krimp(Q): provide Krimp only with the item sets inCTDB as candidates. While we change nothing to the code,we’ll use the notation Krimp_to indicate that Krimp gotonly code table elements as candidates. So, e.g., Krimp_(Q)is the code table that Krimp induces from Q(DB) using theitem sets in CTDB only.Given our general problem statement, we have now haveto prove that Krimp_ satis_es our two require-ments for atransformed algorithm. That is, _rstly, we have to showthat Krimp_(Q) is a good approximation of Krimp(Q). Thatis, we have to show that ADM(Krimp_(Q); Krimp(Q)) =jL(Krimp_(Q)) _ L(Krimp(Q))j L(Krimp(Q))j _ _ for some(small) epsilon. Secondly, we have to show that it is fasterto compute Krimp_(Q) than it is to compute Krimp(Q). Giventhat Krimp is a heuristic algorithm, a formal proof of thesetwo requirements is not possible. Rather, we’ll report onextensive tests of these two requirements.5 The Experiments In this section we describe our experi-

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mental set-up.First we briey describe the data sets we used. Next wediscuss the queries used for testing. Finally we describehow the tests were performed.5.1 The Data Sets . To test our hypothesis that Krimp_ is agood and fast approximation of Krimp, we have performedextensive test on 8 well-known UCI [3]data sets, listed in table 1, together with their respectivenumbers of tuples and attributes. These data sets werechosen because they are well suited for Krimp. Some ofthe other data sets in the UCI repository are simply toosmall for Krimp to perform well. MDL needs a reasonableamount of data to be able to function.Some other data sets are very dense. While Krimp per-forms well on these data sets, choosing them would haveturned our extensive testing prohibitively time-consum-ing.Note that all the chosen data sets are single table Dataset#rows #attributes Heart 303 52Iris 150 19 Led7 3200 24 Pageblocks 5473 46 Pima 786 38Tictactoe 958 29 Wine 178 68

Table 1: UCI data sets used in the experiments.data sets. This means, of course, that queries involvingjoins can not be tested in the experiments. The reason forthis is simple: we have already tested the quality of Krimp_in earlier work [10]. The algorithm introduced in that paper,called R-Krimp, is essentially Krimp_;we’ll return to this topic in the discussion section.5.2 The Queries To test our hypothesis, we need to con-sider randomly generated queries. On _rst sight this ap-pears a daunting task. Firstly, because the set of all pos-sible queries is very large. How do we determine a repre-sentative set of queries? Secondly, many of the generatedqueries will have no or very few results. If the query hasno results, the hypothesis is vacuously true.If the result is very small, MDL (and Krimp) doesn’t per-form very well.Generating a representative set of queries with a non-trivialresult set seems an almost impossible task.Fortunately, relational query languages have a useful prop-erty: they are compositional. That is, one can combinequeries to form more complex queries. In fact, all queriesuse small, simple, queries as building blocks.For the relational algebra, the way to de_ne and combinequeries is through well-known operators: pro-jection (_),selection (_), join (on), union ([), intersec-tion (\), andsetminus (n). As an aside, note that in principle the Carte-sian product (_) should be in the list of operators ratherthan the join. Cartesian prod-ucts are, however, rare inpractical queries since their results are often humonguousand their interpretation is at best di_cult. The join, in con-trast, su_ers less from the _rst disadvantage and not fromthe second. Hence, our ommission of the Cartesian prod-ucts and addition of the join.

So, rather than attempting to generate queries of arbitrarycomplexity, we generate simple queries only.That is, queries involving only one of the operators _, _, [,\, and n. How the insight o_ered by these exper-imentscoupled with the compositionality of relational algebraqueries o_ers insight in our hypothesis for more generalqueries is discussed in the discussion section.5.3 The Experiments The experiments preformed for eachof the operators on each of the data sets were generatedas follows.Projection: The projection queries were generated by ran-domly choosing a set X of n attributes, for n 2 f3; 5g. Thegenerated query is then _X. For this case, the code tableelements generated on the complete data set were alsoprojected on X.The rationale for using a small sets of attributes ratherthan larger ones is that these projections are the mostdisruptive. That is, the larger the set of attributes pro-jected on, the more the structure of the table remains intact. Given that Krimp induces this structure, projectionson small sets of attributes are the best test of our hypoth-esis.Selections: The random selection queries were again gen-erated by randomly choosing a set X of n attributes, withn 2 f1; 2; 3; 4g. Next for each random attribute Ai a randomvalue vi in its domain Di was chosen. Finally, for each Ai inX a random _i 2 f=; 6=g was chosen The generated queryis thus _(VAi2X Ai_ivi).The rationale for choosing small sets of attributes in thiscase is that the bigger the number of attribute sets se-lected on, the smaller the result of the query becomes. Toosmall result sets will make Krimp perform badly.Union: For the union queries, we randomly split the datasetD in two parts D1 and D2, such that D = D1 [D2; note thatin all experiments D1 and D2 have roughly the same size.The random query generated is, of course, D1 [ D2.Krimp yields a code table on each of them, say CT1 andCT2. To test the hypothesis, we give Krimp_the union ofthe item sets in CT1 and CT2.

In practice, tables that are combined using a union may ormay not be disjoint. To test what happens with variouslevel of overlap between D1 and D2, we tested at overlaplevels from f0%; 33:3%; 50%g.intersection: For the intersection queries, we again ran-domly split the data set D into two overlapping parts D1and D2. Again, such that D = D1 [ D2 and again in allexperiments D1 and D2 have roughly the same size. Therandom query gener-ated is, of course, D1 \ D2.Again Krimp yields a code table on each of them, say CT1and CT2. To test the hypothesis, we give Krimp_ the unionof the item sets in CT1 and CT2.The union of the two is given as either of one might havegood codes for the intersection. The small raise in thenumber of candidates is o_set by this potential gain.

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In this case the overlap levels tested were from f33:3%;50%; 66:6%g.Setminus: Selection queries can, of course, be seen as akind of setminus queries. They are special, though, in thesense that they remove a well described part of the data-base.To test less well structured setminus operations, we sim-ply generated random subsets of the data set. The sizes ofthese subsets are chosen from f33:3%; 50%; 66:6%g.Each of these experiments is performed ten times on eachof the data sets.6 The Results In this section we give an overview of theresults of the experiments described in the previous sec-tion. Each relational algebra operator is briey discussed inits own subsection.

6.1 Projection The projection results are given in Table 2.The ADM scores listed are the average ADM score overthe 10 projection experiments performed on that data set _the standard deviation of those 10 ADM scores. Similarly,for the Size scores. Note that Size stands for the reductionin the number of candidates.That is, a Size score of 0.2, means that Krimp_ got only20% of the number of candidates that Krimp got for thesame query result.First of all note that most of the ADM scores are in theorder of a few percent, whereas the Size scores are gener-ally below 0.2. The notable exceptions are the scores forthe Iris data set and, in one case, for Led7. Note, however,that for these three averages, the standard deviation isalso very high. As one would expect, this is caused by afew outliers. That is, if one looks at the individual scoresmost are OK, but one or two are very high. Random experi-ments do have their disadvantages.

Moreover, one should note, however, that these_guresare based on randomly(!) selected sets of attributes. Suchrandom projections are about as disruptive of the datadistribution as possible. In other words, it is impressivethat Krimp_ still manages to do so well.The trend is that the bigger the result set is, the smallerboth numbers are. One can see that, e.g., by comparing theresults on the same data set for projections on 3 and 5attributes respectively. Clearly, these di_erences are ingeneral not signi_cant and the trend doesn’t always hold.However, it is a picture we will also see in the other experi-ments.6.2 Selection The selection results are given in Ta-ble 3.Again the scores are the averages _ the standard devia-tions over 10 runs. The resulting ADM scores are nowalmost all in the order of just a few percent. This is all themore impressive if one considers the Size scores.These approximations were reached while Krimp_ gotmostly less than 2% of the number of candidates thanKrimp. In fact, quite often it got less than 1% of the num-

ber of candidates.The fact that Krimp_ performs so well for selections meansthat while Krimp models the global underlying data distri-bution, it still manages capture the \local” structure verywell. That is, if there is a pattern that is important for a partof the database, it will be present in the code table.The fact that the results improve with the number of at-tributes in the selection, though mostly not sig-ni_cantly,is slightly puzzling. If one looks at all the experiments indetail, the general picture is that big-ger query results givebetter results. In this table, this global picture seems re-versed. We do not have a good explanation for this obser-vation.

6.3 Union The projection results are given in Ta-ble 4. Thegeneral picture is very much as with the previous experi-ments. The ADM score is a few percent, while the reduc-tion in the number of candidates is often impressive.The notable exception is the Iris database. The explana-tion is that this data set has some very local structure that(because of minsup settings) doesn’t get picked up in thetwo components; it only becomes apparent in the union.Note that this problem is exaggerated by the fact that wesplit the data sets at random. The same explanation verymuch holds for the_rst Led7 experiment.We already alluded a few times to the general trend thatthe bigger the query results, the better the results.This trend seems very apparent in this table. For, the higherthe overlap between the two data sets, the bigger the twosets are, since their union is the full data set.However, one should note that this is a bit misleading, forthe bigger the overlap the more the two code tables\know”about the \other” data distribution.

6.4 Intersection The projection results are given in Table 5.Like with for the union, the reduction of the number ofcandidates is again huge in general. The ADM scores areless good than for the union, however, still mostly below0.1. This time the Heart and the Led7 databases that arethe outliers. Heart shows the biggest reduction in the num-ber of candidates, but at the detriment of the ADM score.The explanation for these relative bad scores lies again inlocal structures, that have enough support in one or bothof the components, but not in the intersec-tion. That is,Krimp doesn’t see the good candidates for the tuples thatadhere to such local structures. This is witnessed by thefact that some tuples are compressed better by the originalcode tables than by the Krimp generated code table for theintersection. Again, this problem is, in part, caused by thefact that we split our data sets at random.

The ADM scores for the other data sets are more in linewith the numbers we have seen before. For these, the ADmscore is below 0.2 or (much) lower.6.5 Setminus The projection results are given in Table 6.

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Both the ADM scores and the Size scores are very goodfor all of these experiments. This does make sense, each ofthese experiments is computed on a random subset of thedata. If Krimp is any good, the code tables generated fromthe complete data set should compress a random subsetwell.It may seem counter intuitive that the ADM score growswhen the size of the random subset grows. In fact, it is not.The bigger the random subset, the closer its underlyingdistribution gets to the \true” underlying distribution. Thatis, to the distribution that underlies the complete data set.Since Krimp has seen the whole data set, it will pick up thisdistribution better than Krimp_.

7 DiscussionFirst we discuss briey the results of the experiments.Next we discuss the join. Finally we discuss what theseexperiments mean for more general queries.

7.1 Interpreting the Results The Size scores re-ported inthe previous section are easy to interpret.They simply indicate how much smaller the candidatesetbecomes. As explained before, the runtime complex-ity ofKrimp is linear in the number of candidates. So, since theSize score is never below 0.4 and, often, con-siderablylower, we have established our _rst goal for Krimp_. It isfaster, and often far faster, than Krimp.In fact, one should also note that for Krimp_, we do nothave to run a frequent item set miner. In other words, inpractice, using Krimp_ is even faster than suggested bythe Size scores.But, how about the other goal: how good is the approxima-tion? That is, how should one interpret ADM scores? Ex-cept for some outliers, ADM scores are below 0.2. That is,a full-edged Krimp run compresses the data set 20% betterthan Krimp_. Is that good?In a previous paper [15], we took two random sam-plesfrom data sets, say D1 and D2. Code tables CT1and CT2were induced from D1 and D2 respectively.Next we tested how well CTi compressed Dj . For the fourdata sets also used in this paper, Iris, Led7, Pima and,PageBlocks, the \other” code table compressed 16% to18% worse than the \own” code table; the _g-ures forother data sets are in the same ball-park. In other words, anADM score on these data sets below 0.2 is on the level of\natural variations” of the data distri-bution. Hence, giventhat the average ADM scores are often much lower weconclude that the approximation by Krimp_ is good.In other words, the experiments verify our hypoth-esis:Krimp_ gives a fast and good approximation of Krimp. Atleast for simple queries.

7.2 The Join In the experiments, we did not test the joinoperator. We did, however, already test the join in a previ-ous paper [10]. The R-Krimp algorithm introduced in that

paper is Krimp_ for joins only.Given two tables, T1 and T2, the code table is induced onboth, resulting in CT1 and CT2. To compute the code tableon T1 on T2, R-Krimp only uses the item sets in CT1 andCT2. Rather than using, the union of these two sets, forthe join one uses pairs (p1; p2), with p1 2 CT1 and p2 2CT2.While the ADM scores are not reported in that paper, theycan be estimated from the numbers reported there. Forvarious joins on, e.g., the well known _nancial data set,the ADM can be estimated as to be between 0.01 and 0.05.The Size ranges from 0.3 to 0.001; see[10] for details.In other words, Krimp_ also achieves its goals for the joinoperator.

7.3 Complex Queries For simple queries we know thatKrimp_ delivers a fast and good approximation.How about more complex queries?As noted before, these complex queries are built from sim-pler ones using the relational algebra operators.Hence, we can use error propagation to estimate the errorof such complex queries.The basic problem is, thus, how do the approxima-tionerrors propagate through the operators? While we do haveno de_nite theory, at worse, the errors will have to besummed. That is, the error of the join of two se-lectionswill be the sum of the errors of the join plus the errors ofthe selections.Given that complex queries will only be posed on largedatabase, on which krimp performs well. The initial errorswill be small. Hence, we expect that the error on complexqueries will still be reasonable; this is, however, subject tofurther research.8 Related Work While there are, as far as the authors know,no other papers that study the same problem, the topic ofthis paper falls in the broad class of data mining with back-ground knowledge. For, the model on the database, MDB,is used as background knowledge in computing MQ. Whilea survey of this area is beyond the scope of this paper, wepoint out some papers that are related to one of the twoaspects we are interested in, viz., speed-up and approxi-mation.A popular area of research in using background knowl-edge is that of constraints. Rather than trying to speed upthe mining, the goal is often to produce mod-els that ad-here to the background knowledge. Examples are the useof constraints in frequent pattern mining, e.g. [2], and mono-tonicity constraints [6]. Note, how-ever, that for frequentpatter mining the computation can be speeded up consid-erable if the the constraints can be pushed into the miningalgorithm [2]. So, speed-up is certainly a concern in thisarea. However, as faras we know approximation plays no role. The goal is still to_nd all patterns that satisfy the constraints.

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Another use of background knowledge is to _nd un-ex-pected patterns. In [9], e.g., Bayesian Networks of the dataare used to estimate how surprising a frequent pattern is.In other words, the (automatically induced) backgroundknowledge is used _lter the output. In other words, speed-up is of no concern in this approach. Ap-proximation clearlyis, albeit in the opposite direction of ours: the more a pat-tern deviates from the global model, the more interesting itbecomes. Whereas we would like that all patterns in thequery result are covered by our approximate answer. 9Conclusions In this paper we introduce a new problem:given that we have a model induced from a database DB.Does that help us in inducing a model on the result of aquery Q on DB. For a given mining algorithm A, weformalise this problem as the construction of an algorithmA_ such that:

1. A_ gives a reasonable approximation of A when appliedto Q, i.e., A_(Q;MDB) tMQ

2. A_(Q;MDB) is faster to compute than MQ.We formalise the approximation in the _rst point usingMDL.We give a solution for this problem for a particular algo-rithm, viz, Krimp. The reason for using Krimp as our prob-lem instance is threefold. Firstly, from earlier research weknow that Krimp characterizes the underlying data distri-bution rather well; see, e.g.,[14, 16]. Secondly, from earlier research on Krimp in a multi-relational setting, we already know that Krimp is easilytransformed for joins [10]. Finally, Krimp is MDL based,which makes it an easy _t for the problem as formalised.The resulting algorithm is Krimp_, which is actu-ally thesame as Krimp, but gets a restricted input.Experiments on 7 di_erent data sets and many di_erentsimple queries show that Krimp_ yields fast and good ap-proximations to Krimp.Experiments on more complex queries are currently under-way.

References

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Dataset 3 attr 5 attr ADM Size ADM SizeHeart 0.06 _ 0.09 0.2 _ 0.13 0.03 _ 0.03 0.2 _ 0.13Iris 0.24 _ 0.28 0.17 _ 0.12 0.21 _ 0.18 0.14 _ 0.12Led7 0.05 _ 0.1 0.31 _ 0.23 0.38 _ 0.34 0.25 _ 0.19PageBlocks 0.04 _ 0.06 0.23 _ 0.21 0.08 _ 0.06 0.2 _ 0.17Pima 0.04 _ 0.05 0.14 _ 0.13 0.08 _ 0.07 0.23 _ 0.17TicTacToe 0.12 _ 0.09 0.11 _ 0.17 0.09 _ 0.1 0.17 _ 0.11Wine 0.16 _ 0.2 0.10 _ 0.09 0.1 _ 0.11 0.1 _ 0.09Table 2: The results of the projection experiments. The

ADM and Size scores are averages _ standard de-viation

Dataset 1 attr 2 attr 3 attr 4 attrADM Size ADM Size ADM Size ADM SizeHeart 0.04 _ 0.03 0.04 _ 0.11 0.04 _ 0.03 0.02 _ 0.002 0.04 _

0.03 0.003 _ 0.003 0.02 _ 0.02 0.001 _ 0.0004Iris 0.04 _ 0.04 0.09 _ 0.01 0.05 _ 0.05 0.1 _ 0.02 0.04 _ 0.01

0.1 _ 0.01 0.01 _ 0.03 0.1 _ 0.01Led7 0.04 _ 0.06 0.02 _ 0.001 0.04 _ 0.01 0.02 _ 0.001 0.03 _

0.02 0.02 _ 0.001 0.03 _ 0.03 0.02 _ 0.01PageBlocks 0.09 _ 0.07 0.007 _ 0.008 0.05 _ 0.04 0.002 _

0.0002 0.03 _ 0.02 0.002 _ 0.0002 0.02 _ 0.02 0.002 _0.0002

Pima 0.1 _ 0.14 0.01 _ 0.003 0.03 _ 0.02 0.01 _ 0.003 0.03 _0.02 0.01 _ 0.002 0.03 _ 0.02 0.01 _ 0.001

TicTacToe 0.16 _ 0.09 0.01 _ 0.002 0.1 _ 0.028 0.01 _ 0.0020.12 _ 0.04 0.02 _ 0.02 0.08 _ 0.03 0.01 _ 0.005

Wine 0.03 _ 0.03 0.02 _ 0.02 0.02 _ 0.02 0.02 _ 0.02 0.02 _0.01 0.01 _ 0.006 0.02 _ 0.01 0.01 _ 0.005

Table 3: The results of the selection experiments. The ADMand Size scores are averages _ standard deviationDataset 0% 33.3% 50%ADM Size ADM Size ADM SizeHeart 0.07 _ 0.02 0.0001 _ 0.0001 0.04 _ 0.02 0.001 _ 0.000040.03 _ 0.05 0.001 _ 0.0002Iris 0.36 _ 0.11 0.07 _ 0.01 0.37 _ 0.1 0.07 _ 0.007 0.34 _ 0.120.07 _ 0.006Led7 0.38 _ 0.31 0.02 _ 0.005 0.05 _ 0.02 0.03 _ 0.002 0.03 _0.02 0.03 _ 0.002PageBlocks 0.06 _ 0.01 0.002 _ 0.0001 0.04 _ 0.01 0.003 _0.0001 0.02 _ 0.01 0.003 _ 0.0001Pima 0.04 _ 0.03 0.01 _ 0.0006 0.03 _ 0.02 0.02 _ 0.002 0.03 _0.02 0.02 _ 0.002TicTacToe 0.07 _ 0.01 0.009 _ 0.0005 0.03 _ 0.02 0.01 _0.0003 0.01 _ 0.002 0.01 _ 0.0002Wine 0.03 _ 0.01 0.006 _ 0.0003 0.03 _ 0.01 0.008 _ 0.00060.02 _ 0.01 0.008 _ 0.0003

Table 4: The results of the union experiments. The per-centages denote the amount of overlap between the twodata sets. The ADM and Size scores are averages _ stan-dard deviationDataset 33.3% 50% 66.6%ADM Size ADM Size ADM SizeHeart 0.39 _ 0.14 0.0002 _ 0.0001 0.36 _ 0.05 0.0002 _ 0.00010.42 _ 0.17 0.0001 _ 0.0001Iris 0.09 _ 0.08 0.1 _ 0.02 0.08 _ 0.07 0.09 _ 0.02 0.03 _ 0.020.09 _ 0.01Led7 0.5 _ 0.14 0.005 _ 0.002 0.42 _ 0.1 0.007 _ 0.001 0.3 _0.12 0.01 _ 0.001PageBlocks 0.13 _ 0.07 0.001 _ 0.0002 0.09 _ 0.06 0.002 _0.0001 0.07 _ 0.05 0.002 _ 0.0001Pima 0.09 _ 0.06 0.01 _ 0.002 0.09 _ 0.09 0.01 _ 0.003 0.05 _0.06 0.01 _ 0.002TicTacToe 0.2 _ 0.05 0.007 _ 0.002 0.22 _ 0.04 0.005 _ 0.00070.24 _ 0.04 0.004 _ 0.0007Wine 0.1 _ 0.02 0.01 _ 0.005 0.12 _ 0.03 0.005 _ 0.001 0.15 _0.04 0.002 _ 0.0006Table 5: The results of the intersection experiments. Thepercentages denote the amount of overlap between thetwo data sets. The ADM and Size scores are averages _standard deviationDataset 33.3% 50% 66.6%ADM Size ADM Size ADM Sizeheart 0.01 _ 0.01 0.001 _ 0.00007 0.01 _ 0.01 0.001 _ 0.00010.03 _ 0.02 0.002 _ 0.0004iris 0.003 _ 0.006 0.11 _ 0.007 0.005 _ 0.008 0.12 _ 0.01 0.02 _0.02 0.14 _ 0.01led7 0.02 _ 0.02 0.02 _ 0.0002 0.02 _ 0.02 0.02 _ 0.0006 0.06 _0.03 0.02 _ 0.001pageBlocks 0.01 _ 0.004 0.002 _ 0.00004 0.02 _ 0.01 0.002 _0.00003 0.03 _ 0.01 0.003 _ 0.00007pima 0.02 _ 0.01 0.02 _ 0.001 0.01 _ 0.01 0.02 _ 0.001 0.01 _0.02 0.02 _ 0.001ticTacToe 0.06 _ 0.02 0.01 _ 0.0003 0.07 _ 0.02 0.01 _ 0.00050.08 _ 0.02 0.02 _ 0.002wine 0.01 _ 0.007 0.01 _ 0.002 0.02 _ 0.01 0.02 _ 0.005 0.03 _0.02 0.04 _ 0.01

Table 6: The results of the setminus experiments. The per-centages denote the size of the remaining data set.The ADM and Size scores are averages _ standard devia-tion

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Paper

Information System Department, Institut Teknologi Sepuluh Nopember,Kampus Keputih, Sukolilo, Surabaya 60111, Indonesia

AbstractNowadays, forecasting is developed more rapidly because of more systematicaly decision making process in companies.One of the good forecasting characteristics is accuration, that is obtaining error as small as possible. Many currentforecasting methods use large historical data for obtaining minimal error. Besides, they do not pay attention to theinfluenced factors. In this final project, one of the forecasting methods will be proposed. This method is called RegressionDynamic Linear Model (RDLM). This method is an expansion from Dynamic Linear Model (DLM) method, which modela data based on variables that influence it.In RDLM, variables that influence a data is called regression variables. If adata has more than one regression variables, then there will be so many RDLM candidate models. This will make thingsdifficult to determine the most optimal model. Because of that, one of the Bayesian Model Averaging (BMA) methods willbe applied in order to determine the most optimal model from a set of RDLM candidate models. This method is calledAkaike Information Criteria (AIC). Using this AIC method, model choosing process will be easier, and the optimalRDLM model can be used to forecast the data.BMA-Akaike Information Criteria (AIC) method is able to determineRDLM models optimally. The optimal RDLM model has high accuracy for forecasting. That can be concluded from theerror estimation results, that MAPE value is 0.62897% and U value is 0.20262.

Keyword : Forecasting, Regression variables, RDLM, BMA, AIC

Saturday, August 8, 200913:55 - 14:15 Room L-211

FORECASTING USING REGRESSION DYNAMIC LINIER MODEL

1. IntroductionNowadays, forecasting has developed more rapidlybecause of the more systematically decision making in aorganization or company. One of the good forecastingcharacteristic is from accuration, and should get errorthat is as minimal as possible. Usually, forecasting justestimates based on historical data only withoutconsidering external factors that might influence the data.Because of that, in this paper will be proposed a methodthat takes all external factors into consideration, thismethod is called Regression Dynamic Linear Models(RDLM), with Bayesian Model Averaging (BMA) appliedin order to choose the most optimal model. By using thismethod, the forecast results will have high accuracy.(Mubwandarikwa et al., 2005).

2. The MethodThere are four steps to forecast a data using RDLMmethod, i.e. : forming candidate models, choosing optimal

model, forecasting using optimal model, and measuringaccuracy of optimal model.

2.1 Dynamic Linear Models (DLM)Dynamic Linear Model is an extension of state-spacemodeling on prediction and dynamic system control(Aplevich, 1999). State-space model of time series containsdata generating process with state (usually shown byvector of parameter) that can change over time. This stateis only observed indirectly, as far as values of time seriesthat are obtained as function of state in correspond period.DLM base model at all time t is described by evolution /system and observation equation. The equation forms areas follow :o Observation equation :

Wiwik Anggraeni(University of Indonesia)

Danang Febrian(University of Indonesia)

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,tttt vFY += θ

where [ ]tt VNv ,0~ (1)o System equation :

,1 tttt G ωθθ += −

where [ ]tt WN ,0~ω (2)o Initial information :

[ ]000 ,~ CmNθ (3)DLM can be explained alternatively with 4 sets asfollow :

jtttttt WVGFjM },{)( ,,= j = 1,2,... (4) Where at time t, :

o tθ is state vector at time t.

o tF is known regression variable vector.

o tv is observation noise that has Gaussian distribution

with zero mean and known variance tV , where it represents estimation and error trial of changing

observation of tY .

o tG is state evolution matrix, it describes deterministic mapping of state vector between time t – 1 and t.

o tω is evolution noise that has Gaussian distribution with zero mean and variance matrix Wt, where it represents changing in state vector.

2.2 Regression Dynamic Linear Models (RDLM)Regression Dynamic Linear Model (RDLM) is an extensionof DLM, which RDLM considers regression variables(regressor) in modeling process. For example, for timeseries data that has regressors X1, X2, then it will haveseveral possible models, that are M1( ,X1), M2( ,X2) andM3( ,X1,X2). For time t, t = 1,2,… Regression DynamicLinear Model (RDLM), ,(j = 1,2,...,k), represents a basetime series model with 4 observations, which can beidentified by 4 sets, where :

o jj XpXF ),...,( 1= is regression vector (1 x p), ijX

is ith variable regression (i =1,2,...,p) which for 1X has value of 1.

o jG is system evolution matrix (p x p) with the valueof

)(nIG j = identity matrix.

o jV is observation variance of .

o is system evolution variance matrix (p x p) which is estimated using discount factors, for ith time : (5) Discount factors are determined by checking off model to determine the optimal values. Optimal value for trend component , seasonality , variance and regression (Mubwandarikwa et al., 2005).

2.2.1. RDLM Sequential UpdatingEstimation of state variables () can not be done directly atall times, but by using information from data which updatefrom time t-1 to t is performed using Kalman Filter. Forfurther information, see West and Harrison (1997).Take as example describes all information from past timesuntil time t and is data at time t.Assume that :(6) Equation (2) and (6) have Gaussian distribution, so linear combinations of both of them can be formed and produce prior distribution that is :(7) Then from equation (1) and (7), forecast distribution can be obtained, that is :(8) where From forecast distribution at equation (8), forecast result for can be obtained using :(9) By using Kalman Filter, posterior distribution can be obtained :(10)

where

with

All the steps above solve recursive update of RDLM andcan be summaried as following :1. determining model by choosing .2. setting initial values of .3. forecasting using equation (9) .4. observing and updating using equation (10).5. back to (c), then substituting t+1 with t

2.3 Bayesian Model Averaging of RDLMIn RDLM method, there are many candidate models. Fordetermining the most optimal model, one of BMA methodis used, that is Akaike Information Criteria (AIC).

2.3.1 Akaike Information Criteria (AIC)Akaike Information Criteria (AIC) by Akaike (1974)originates from maximum (log-)likelihood estimate (MLE)from error variance of Gaussian Linear regression model.Maximum (log-) likelihood model can be used to estimateparameter value in classic linier regression model. AICsuggests that from a class of candidate models, choosemodel that minimize : (11) Where for jth model : o is likelihood. o p is number of parameters in model. This method chooses model that gives best

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estimates asymptotically (Akaike, 1974) in explanation of Kullback-Leibler. Akaike weight can be estimated by defining :(12) where minAIC is the smallest value of AIC in a set

of models. Likelihood from every model conditionalon data and set of models. Then Akaike weightcan be estimated using equation :

(13) where k is number of possible models inconsideration and the rest of defined modelscomponent. (Turkheimer et al., 2003)

2.4 Error EstimationFor knowing the accuration of forecasting model, it canbe seen from error estimation result. According toMakridakis et al., 1997, several methods in forecast errorestimation that can be used are as following :

o Mean Absolute Percentage Error (MAPE)

MAPE is differences between real data and forecast resultthat is divided with forecast result then is absoluted andthe result is on percent value. A model has excellentperformance if MAPE value lies under 10%, and goodperformance if MAPE value lies between 10% and 20%(Zainun dan Majid, 2003).

(14)

o Theil’s U statistic

U statistic is performance comparation between aforecasting model with naïve forecasting, that predictsfuture value is equivalent with real value one time before.Comparation takes correspond ratio with RMSE (rootmean squared errors), that is square root of averagesquared differences between prediction and observation.As the main rule, forecasting method that has Theil’s Uvalue larger than 1 is not effective. (15) where for all methods,= data, = forecasting result.

3. Implementation and AnalysisSeveral trial test that have been done are choosingoptimal model, forecasting optimal model, testing AICperformance and comparing DLM with RDLM.To do the trial tests, world commodity price index data isused. This data contains many kinds world commodityincluding food, gas, agriculture, and many kinds of metal.Several variables used are :1. Rice price index (D).2. Fertilizer price index (X1).3. Agriculture tools price index (X2).4. Refined fuel oil price index (X3).This data is from 1980 until 2001. The target forecastdata is the first variable that is rice price index, with

regression variables fertilizer price index, agriculture toolsprice index, and refined fuel oil price index. Plot of riceprice index is shown on figure 1.

3.1 Model ChoosingSince data is influenced by 3 variables, then there are 7RDLM candidate models, that are :M1(D,X1),M2(D,X2),M3(D,X3),M4(D,X1,X1),M5(D,X1,X3),M6(D,X2,X3),M7(D,X1,X2,X3).After implementing AIC, then weight of every model isobtained as following :

From the table above, it can be seen that M5 has thelargest weight, so M5 is the most optimal model.

3.2 Optimal Model ForecastingFrom the previous section, M5 has been chosen as themost optimal model which will be used for forecasting.The forecasting result of M5 is shown on figure 2.

Figure 2 RDLM Forecasting

Figure 1 Data Plot

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From the forecasting result, then the accuration iscalculated and shown on the following table

Table 2 Error Estimation Result

From those calculation, it can be seen that RDLM modelhas excellent performance in forecasting. This is becauseits MAPE value lies under 10%, that is 0.62897%. FromTheil’s U point of view, this model is effective since its Uvalue is under 1.

3.3 Testing AIC PerformanceIn order to analyze AIC performance, every modelaccuration will be compared, then it can be seen whethermodel that has been chosen by AIC is a model with thesmallest error. Accuration of every model is shown on thefollowing table :

Table 3 Errors of Every Model

It can be seen from the table above that M5 has thesmallest error, so it can be concluded that AIC methodworks well in choosing model.

3.4 Comparation Between RDLM and DLM PerformanceIn this section, RDLM and DLM will be compared to provethat RDLM method work better than DLM method. Thiscan be done by comparing DLM model with the mostoptimal RDLM model that is M5. Forecasting result ofboth of those models is plotted on figure 3.

Figure 3 RDLM and DLM ForecastingFrom the forecasting result, error estimation will be doneand shown on the following table.

Table 4 RDLM and DLM Errors

From the above table, it can be seen that RDLM methodhas smaller error than DLM model, from the MAPE andTheil’s U value.

4 . ConclusionSeveral conclusions than can be taken about applicationof BMA-Akaike Information Criteria (AIC) in RDLM(Regression Dynamic Linear Model) forecasting methodis as follows :1. Forecasting using RDLM (Regression Dynamic

Linear Model) has high accuration as long as thechosen model is the most optimal model.

2. BMA-Akaike Information Criteria (AIC) method isproven to determine the RDLM models optimally.

3. Forecasting using RDLM method has better resultthan normal DLM method as long as the RDLM modelis the most optimal model.

4. Using rice price index data on 1997 – 2001, RDLMmethod works 48% better than DLM method judgingfrom MAPE value, and 46% better judging fromTheil’s U value.

5. Daftar Pustaka

Akaike, H. (1974), A new look at the Statistical modelidentication. IEEE Trans. Auto. Control, 19, 716-723.

Aplevich, J., 1999. The Essentials of Linear State SpaceSystems. J. Wiley and Sons.

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Grewal, M. S., Andrews, A. P., 2001. Kalman Filtering:Theory and Practice Using MATLAB (2nd ed.). J. Wileyand Sons.

Harvey, A., 1994. Forecasting, Structural Time-seriesModels and the Kalman Filter. Cambridge UniversityPress.

Mubwandarikwa, E., Faria A.E. 2006 The GeometricCombination of Forecasting Models Department ofStatistics, Faculty of Mathematics and Computing, TheOpen University

Mubwandarikwa, E., Garthwaite, P.H., dan Faria, A.E., 2005.Bayesian Model Averaging of Dynamic Linear Models.Department of Statistics, Faculty of Mathematics andComputing, The Open UniversityTurkheimer, E., Hinz, R. and Cunningham, V., (2003), Onthe undesirability among kinetic models: from modelselection to model averaging. Journal of Cerebral BloodFlow & Metabolism, 23, 490-498.

Verrall R. J. 1983. Forecasting The Bayesian The CityUniversity, London

West , Mike. 1997. Bayesian Forecasting, Institute ofStatistics & Decision Sciences Duke University

World Primary Commodity Prices.(2002). Diambil padatanggal 23 Mei 2008 dari http://www.economicswebinstitute.org

Yelland, Phillip M. & Lee, Eunice. 2003, ForecastingProduct Sales with Dynamic Linear Mixture Models. SunMicrosystem

Zainun, N. Y., dan Majid, M. Z. A., 2003. Low Cost HouseDemand Predictor. Universitas Teknologi Malaysia.

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PaperSaturday, August 8, 2009

14:20 - 14:40 Room L-210

EDUCATIONAL RESOURCE SHARING IN THE HETEROGENEOUSENVIRONMENTS USING DATA GRID

Faculty of Computer Science, University of Indonesiaemail: [email protected] 1, [email protected] 2

AbstractEducational resources usually reside in the digital library, e-learning and e-laboratory systems. Many of the systemshave been developed using different technologies, platforms, protocols and architectures. These systems maintain alarge number of digital objects that are stored in many different storage systems and data formats with differences in:schema, access rights, metadata attributes, and ontologies. This study proposes a generic architecture for sharingeducational resources in the heterogeneous environments using data grid. The architecture is designed based on thetwo common types of data: structured and unstructured data. This architecture will improve the accessibility, integrationand management of those educational resources.

Keywords: resource sharing, data grid, digital library

1. Introduction

Currently, the increasing social demands on high qualityeducational resources of higher education cannot befulfilled only by the available educators and conventionallibraries. With the advances of information technology,many learning materials and academic journals created byuniversities have been converted into digital objects. Therapid growth of Internet infrastructure accelerates thetransformation of conventional libraries and learning tothe digital libraries and e-learning. This transformationgreatly affects the way of people to get information andlearn. Accessing information and learning now can bedone from anywhere at any time.

Since many digital library, e-learning and e-laboratorysystems have been developed using differenttechnologies, platforms, protocols and architectures, theywill potentially introduce the problem of informationislands. In order to address this problem, some previousworks [1][2][3][4] proposed the use of grid technologythat has the capability of integrating the heterogeneousplatforms. However, most of them considered that theshared resources are only files or unstructured data.Educational resources consist of not only unstructureddata, but also structured data. Much information such as

the metadata describing the shared digital objects andthe XML formatted documents is stored in a database.This information also needs to be shared with othersystems.In this study, we propose a generic architecture for sharingeducational resources in heterogeneous environmentusing data grid. We also show how this architectureapplies in the digital libraries using Indonesian HigherEducation Network (INHERENT) [5].

2. Inherent

INHERENT (Indonesian Higher Education Network) [5] isa network backbone that is developed by Indonesiangovernment to facilitate the interconnection among thehigher education institutions (HEIs) in Indonesia. Theproject was proposed by the directorate of highereducation. Started on July 2006, currently it connects 82state HEIs, 12 regional offices of the coordination ofprivate HEIs, and 150 private HEIs (see Figure 1).All state HEIs in Java are connected by STM-1 NationalBackbone with the bandwidth of 155 Mbps. Other citiesin the other islands use 8-Mbps leased line and 2 MbpsVSAT connections.

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Figure 1. Indonesian Higher Education Network in 2009[5]

This network has been used for various educationalactivities including video-conferencing and distancelearning. Every university can build their own digitallibraries and learning management systems (LMSs) andthen publish their educational resources through thenetwork. Although the resources can be shared to eachanother via FTP or web servers, the systems (digitallibraries and LMSs) cannot provide an integrated view tousers. Users still have to access every digital librarysystems in order to find the resources required by them.This network has a potential of sharing variouseducational resources using data grid.3. Data Grid

Data grid is one of the types of grid technologies. Theother types are computational and access grid. Originally,the emphasis of grid technology lay in the sharing ofcomputational resources [6]. Technological and scientificadvances have led to an ongoing data explosion in manyfields. Data are stored in many different storage systemsand data formats with different schema, access rights,metadata attributes, and ontologies. These data also needto be shared and managed. The need then introduces anew grid technology, namely data grid. There are someexisting data grids. In the following, we will overview twoof them (iRODS and OGSA-DAI) and highlight theirfeatures to address the need.

iRODS

iRODS (Integrated Rule-Oriented Data System) [7] is asecond generation data grid system providing a unifiedview and seamless access to distributed digital objectsacross a wide area network. It is extended from the StorageResource Broker (SRB) that is considered as the firstgeneration data grid system. Both SRB and iRODS aredeveloped by the San Diego Supercomputing Center(SDSC).

Classified as the first generation of the data grid, SRB ismainly focused on providing a unified view overdistributed storages based on logical naming conceptsusing the client-server architecture. The conceptsfacilitated the naming and location transparency whereusers, resources, data objects and virtual directories wereabstracted by logical names and mapped onto physicalentities. The mapping is done at run time by theVirtualization sub-system. The information of the mappingfrom the logical name to physical name is maintainedpersistently in a database system called the MetadataCatalog. The database also maintains the metadata of thedata objects that use the schema of attribute-value pairand the states of data and operations. Built upon thislogical abstraction, iRODS takes one level higher byabstracting the data management process itself calledpolicy abstraction.Whilst the policies used for managing the data at the serverlevel in SRB are hard-coded, iRODS uses anotherapproach, Rule-oriented Programming (ROP), to make thecustomization of data management functionalities mucheasier. Rules are explicitly declared to control theoperations performed when a rule is invoked by a particulartask. In iRODS, these operations are called micro servicesand implemented as functions in C programming language.

Figure 2. iRODS Architecture [7]

Figure 2 displays the iRODS architecture with its mainmodules. The architecture differentiates between theadministrative commands needed to manage the rules, andthe rules that invoke data management modules. When auser invokes a service, it fires a rule that uses theinformation from the rule base, status, and metadatacatalog to invoke micro-services [8]. The micro-serviceseither change the metadata catalog or change the resource(read/write/create/etc).Figure 3 illustrates a scenario when a client sends a queryasking for a file from an iRODS zone. Firstly, he connectsto one of iRODS servers (for example server A) using aclient application and sends the criteria of the file needed

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(e.g. based on the metadata, filename, size, etc). The requestis directed to server A that will find the file usinginformation available in Metadata catalog. The query resultis sent back to the client. If he/she wants to get the file,server A asks the catalog server which iRODS server thatstores the file (for example in the server B). Server A thencommunicates with server B to request the file. Server Bapplies the rules related with the request. The rules canbe the process of authorization (whether the client has aprivilege to read the file) and sending the file to the clientusing iRODS native protocol. The client is not aware ofthe location of the file. This location transparency ishandled by the grid.

Figure 3. A client asks for a file from an iRODSdata grid [7]

Based on the explanation above, we conclude that iRODSfocuses on managing unstructured data objects such asfiles. Although it can also access structured dataresources, its orientation is mainly on distributed filemanagement. However, it also uses structure data(relational database) to manage the metadata of the dataobjects, the states of data and the states of operations.The metadata can potentially be integrated using theOGSA-DAI data grid.

OGSA-DAI

OGSA-DAI (Open Grid Services Architecture – DataAccess and Integration) [9] is a middleware software thatallows structured data resources, such as relational orXML databases, from multiple, distributed,heterogeneous and autonomously managed data sourcesto be easily accessed via web services. It focuses on caseswhere the assembly of all the data into a single datawarehouse is inappropriate [9].

Figure 4. An overview of OGSA-DAI components [10]

OGSA-DAI is designed to enable sharing of dataresources to make collaboration that supports:a. Data access service, which allows to access

structureddata in distributed heterogeneous dataresources.

b. Data transformation service, which allows to exposedata in schema X to users as data in schema Y

c. Data integration service, which allows to exposemultiple databases to users as a single virtual database

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d. Data delivery service, which allows to deliver data towhere it’s needed by the most appropriate means,

uch as Web service, email, HTTP, FTP and GridFTPOGSADAI has adopted a service oriented architecture(SOA) solution for integrating data and grids through theuse of web services. The role of OGSA-DAI in a service-based Grid, illustrated in Figure 4, involves interactionsbetween several following components [10]:

a. OGSA-DAI data service: a web service that implementsvarious port types allowing the submission of requestsand data transport operations

b. Client: an entity that submits a request to the OGSA-DAI data service. A request is in the form of a performdocument that describes one or more activities to becarried out by the service.

c. Consumer: a process, other than the client, to whichan OGSA-DAI service delivers data.

d. Producer: a process, other than the client, that sendsdata to an OGSA-DAI data service.

When a client wants to make a request to an OGSA-DAIdata service, it invokes a web service operation on thedata service using a perform document. A performdocument is an XML document describing the requestthat the client wants to be executed, defined by linkingtogether a sequence of activities. An activity is an OGSA-DAI construct corresponding to a specific task that shouldbe performed. The output of one activity can be linked tothe input of another to perform a number of tasks insequence. A range of activities is supported by OGSA-DAI, falling into the broad categories of relational activities,XML activities, delivery activities, transformationactivities and file activities. Furthermore, the activity is anOGSA-DAI extensibility point, allowing third parties todefine new activities and add them to the ones supportedby an OGSA-DAI data service.

OGSA-DAI focuses on managing heterogeneousstructured data resources. Although it can also accessthe unstructured data using file transfer, its orientation isdatabase) management.

According to the two existing data grid middlewares, thereollowing section, we propose a system architecture thataccommodates these two kinds of data.

4. The Proposed Architecture

In this study, a generic architecture for sharing educationalresources in heterogeneous environment using data grid

middleware is proposed based on the two types of data:structured and unstructured data.

From the perspective of computer processing, the digitalobjects are merely data. Generally, data can be classifiedinto two categories: unstructured and structured data.Unstructured data consists of any data stored in anunstructured format at an atomic level. There is noconceptual definition and no data type definition in theunstructured content. Furthermore, unstructured data canbe divided into two basic categories: bitmap objects (suchas video, image, and audio files) and textual objects (suchas spreadsheets, presentations, documents, and email).Both of them can be treated as a string of bits. Theunstructured data is usually managed by operatingsystem. Structured data has schema information thatdescribes its structure. The schema can be separated fromthe data (such as in relational database) or it can be mixedwith the data (e.g. XML format). The structured data isusually managed by a database management system. Thesystem facilitates the processes of defining, constructing,manipulating, and sharing the data among various usersand applications [11]. This differentiation of the nature ofdata brings into different treatment when the variousformats of data and storage systems are handled in themiddleware layer.

Figure 5 shows the proposed architecture that utilizes thesimilar hierarchy used in [12] but it is applied in managingheterogeneous data resources. The architecture consistsof three layers: data layer, data grid middleware layer andapplication layer.

At the data layer, the various data resources in theheterogeneous file systems and storage systems can bejoined into one large data collection. We distinguishbetween structured and unstructured data because of theirdifferent inherent characteristics. At the data gridmiddleware layer, the data virtualizations for each datatype are separated. The unstructured data are virtualizedby file-oriented data grid middleware, such as SRB andiRODS, while the structured data virtualization is handledby database-oriented data grid middleware, such as OGSA-DAI.

Based on the analysis of the file-orientated data grids(such as SRB and iRODS), the unstructured datavirtualization provides the following basic services [13]:a. Data storage and replication service, which allows to

store any type of digital object content and to replicateit into several other resources. The service isindependent of the content type because only theclients need to be aware of the content internal formatand structure.

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b. Composition and relation service, which allows todefine various relations between digital objects andto define multiple groups of related objects. Thoserelations may be used to create complex digitalobjects, to show parent/child relationships betweenobjects or to create collections of digital objects.

c. Search service, which allows to search in previouslydefined sets of digital objects. The search can bebased on the query matching with the metadatacatalog.

d. Metadata storage service, which allows to storemetadata describing digital objects. One object canbe described in many metadata records. The metadataalso records information associated with replication.These records can be utilized for the search service.Usually, database systems are used to store andmanage the metadata. Furthermore, some databasesystems containing metadata can be integrated usingstructured data virtualization components of the datagrid middleware.

The structured data virtualization provides the four basicservices as described in the section of OGSA-DAI.At the application layer, data-intensive applications, suchas e-learning management systems and digital libraries,can utilize the two data virtualizations in order to publishand share their digital content objects.Figure 6 shows a typical implementation of the proposedarchitecture for digital library. Every digital library sitesthat are registered in the Integrated Higher EducationDigital Library Portal manage their own data resources

consisting of the collection of digital objects andstructured data (relational database and XML). The digitalobjects are stored in various storage systems that aremanaged by iRODS storage servers. Since all of iRODSservers are registered in one zone, namely Zone IDL(Indonesian Digital Libraries), the digital objects can bereplicated among the servers. Some files of site A can bereplicated to the servers of site B, and vice versa. Themetadata catalog servers in both sites will contain thesame information of all collected educational resources.

Some digital library systems store index files for the useof searching in relational databases. The systems can alsomanage some kinds of educational resources formatted inXML (e.g. semi-structured documents) using native XMLdatabases. All of this information can be accessed andintegrated by the Integrated Higher Education DigitalLibrary Portal using OGSA-DAI. Therefore, a user can dodistributed searching for files stored in all resources ofboth sites.

integrated system also enables a user to get all resourcescloser to him if the resources are already replicated tosome locations.Since all sites are connected in INHERENT with high-speed bandwidth, there is no need for the IntegratedHigher Education Digital Library Portal to harvest themetadata from all member sites such as proposed in [4].No central metadata repository is required. This ensuresthat the query results of distributed searching will alwaysbe up to date because they come from the local queryprocessing of each member sites.

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Figure 6. A typical implementation of the proposed architecture for digital library

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5. Conclusion

In this study, we propose a generic architecture for sharingeducational resources in the heterogeneous environment.The architecture distinguishes the managed data into twocategories, namely structured and unstructured data. Thedata grid middleware used for virtualization is separated

based on the two categories of data. In our design, weThe combination of the two data grids completely handlesall kinds of data types. Hence, this architecture canimprove the accessibility, integration and management ofthose educational resources.

6. References

[1] Yang, C.T., Hsin-Chuan Ho. Using Data GridTechnologies to Construct a Digital LibraryEnvironment. Proceedings of the 3rd InternationalConference on Information Technology: Researchand Education (ITRE 05), pp. 388-392, NTHU,Hsinchu, Taiwan, June 27-30, 2005. (EI)

[2] Candela, L., Donatella Castelli, Pasquale Pagano,Manuele Simi. Moving Digital Library ServiceSystems to the Grid. Springer-Verlag. 2005.

[3] Sebestyen-Pal, G., Doina Banciu, Tunde Balint, BogdanMoscaiuc, Agnes Sebetyen-Pal. Towards a GRID-based Digital Library Management System. InDistributed and Parallel Systems p77-90. Springer-Verlag. 2008.

[4] Pan, H. Research on the Interoperability Architectureof the Digital Library Grid. 2007. in IFIPInternational Federation for InformationProcessing, Volume 251, Integration andInnovationOrient to E-Society Volume l, Wang, W.(Eds), (Boston: Springer), pp. 147 154.

[5] Indonesian Higher Education Network (INHERENT).http://www.inherent-dikti.net

[6] Foster, I., Carl Kesselman. The Grid: Blueprint for aNew Computing Infrastructure. 2nd Edition.Morgan Kaufmann. 2006.

[7] iRODS (Integrated Rule-Oriented Data System. https://www.irods.org

[8] Weise, A., Mike Wan, Wayne Schroeder, Adil Hasan.Managing Groups of Files in a Rule OrientedData Management System (iRODS). Proceedingsof the 8th International Conference onComputational Science, Section: Workshop onSoftware Engineering for Large-Scale Computing,Krakow, Poland. 2008

[9] OGSA-DAI (Open Grid Services Architecture - DataAccess and Integration). http://www.ogsadai.org.uk/index.php

[10] Chue Hong, N.P., Antonioletti, M., Karasavvas, K.A.and Atkinson, M. Accessing Data in Grids UsingOGSA-DAI, in Knowledge and Data Managementin GRIDs, p3-18, D. Talia, A. Bilas, M.D. Dikaiakos(Eds.), 2007, ISBN: 978-0-387-37830-5

[11] Elmashri, R., Shamkant B. Navathe. Fundamental ofDatabase Systems. 5th Edition. Addison Wesley.2006.

[12] Coulouris, G., Jean Dollimore, Tim Kindberg.Distributed Systems : Concepts and Design. 4th

edition. Addison Wesley. 2005.

[13 ]Kosiedowski, M.,Mazurek, C., Stroinski, M., Werla,M. and Wolski, M. Federating Digital LibraryServices for Advanced Applications in Scienceand Education, Computational Methods inScience and Technology 13(2), pp. 101-112.December, 2007

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Paper

Untung RahardjaSTMIK RAHARJARaharja Enrichment Centre (REC) Tangerang - Banten, Indonesia

[email protected]

Edi WinarkoGADJAH MADA UNIBERSITYFaculty of Mathematics and Natural SciencesYogyakarta, Indonesia

[email protected]

Muhamad YusupSTMIK RAHARJARaharja Enrichment Centre (REC) Tangerang - Banten, Indonesia

[email protected]

AbstractGoal implementation system that is web-based information so that users can access information wherever and wheneverdesired. Absensi Online (AO) is a web-based information system that functions to serve the students and lectures havebeen applied in the Universities Raharja. Lecturer in attendance to the presence of lecturers and students in classroomson a lecture. The system can still be read and accessed by all users connected to the network. However, this may lead tothe occurrence of Breach or deceptions in the presence of the attendance. There is access to the prevention of entry is notthe right solution to be used when the information should still be readable and accessible by all users connected to thenetwork. Security system must be transparent to the user and does not disrupt. With behavior detection, using the conceptof data mining Absensi Online (AO) can be done with the wise. The system can detect and report if there is an indicationthings are negative. Initialization of the information still can be enjoyed by all users connected to the network withoutrestriction access entrance. In this article, the problems identified in the Absensi Online (AO) in the Universities Raharja,critical review related to the behavior detection, detection and behavior is defined using the concept of data mining asa problem-solving steps and defined the benefits of concept. There is a behavior detection using the concept of datamining on a web-based information systems, data integrity and accuracy can be guaranteed while the system perfor-mance be optimized so that the life of the system can continue to progress well.

Index Terms— behavior detection, data mining, Absensi Online (key words)

Saturday, August 8, 200914:20 - 14:40 Room L-212

Behavior Detection Using The Data Mining

I. IntroductionRaharja university that are moving in the field of computerscience and located in Banten Province is located only 10(ten) minutes from the International Airport Soekarno -Hatta. Many awards that have been achieve in, one ofwhich is winning the WSA 2009 - Indonesia E-Learningand Education of Intranet Product Category Raharja Mul-timedia Edutainment (RME). At this time Raharja univer-sity has to improve the quality and quality through ac-creditation certificate National Accreditation Board ofHigher Education (BAN-PT) of which states that the pro-gram of Diploma 3 in Komputerisasi Akuntansi AMIKRaharja Informatika accredited A. In addition, the univer-

sity has entered Raharja ranked top 100 universities andcolleges in the Republic of Indonesia.Universities Raharja has 4 (four) Platform IT E-learningconsists of the SIS (Student Information Services), RME(Raharja Multimedia Edutainment), INTEGRAM (Inte-grated Marketing Raharja), and GO (Green Orchestra) isthe instrument to be Raharja University campus excellentfit with the vision that is superior to the universities thatproduce graduates who competent in the field of informa-tion systems, informatics techniques and computer sys-tems and has a high competitiveness in the globalizationera.

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Figure 1. 4 Pilar IT Perguruan Tinggi Raharja

SIS (Student Information Services) is a software speciallydesigned to improve the quality of service to students andwork to provide information on: student lecture schedulewas selected based on the semester, Kartu Hasil Studi(KHS), Index table Comulative Achievement (GPA), a listof values, provides a service creation form that can beused by student activities in lectures and so quickly and inreal time [1].Green Orchestra (GO) is a financial instrument accountingIT system at the University Raharja to provide serviceexcellence to the online Personal Raharja to give comfortto the cash register for staff and students in terms of speedand accuracy of data services [2].INTEGRAM (Raharja Integrated Marketing) is a web-basedinformation system designed specifically to serve the pro-cess of acceptance of new students at the UniversityRaharja. With INTEGRAM, student acceptance of a newfaster (service excellence) and the controlling can be donewell [3].RME (Raharja Multimedia Edutainment) the understand-ing that Raharja Universities in developing the concept ofthe learning process based multimedia entertainment arepacked so that the concept of Interactive Digital Multime-dia Learning (IDML) that touches five senses strengthsinclude text, images, and to provide a voice in the processof learning to all civitas academic and continuously makeimprovements (continuous improvement) towards perfec-tion in the material of teaching materials, which is alwaysevolving as the progress and development of technology[4].RME can facilitate civitas academic to obtain informationabout the SAP, and Syllabus of teaching materials, facultycan easily mold to the presentation materials in to RMEpresented to students, and the system controlling in theacademic field to make the decision easy [5].Absensi Online (AO) is part of the development and Raharja

Multimedia Edutainment (RME), which designed and imple-mented to improve services and better training of studentsand lecturers to be able to appreciate more time in thelecture [6].But, whether the system has been developed which is ableto provide convenience in terms of reporting to the facultyand students considered to violate the order and lecturediscipline or in the process of learning activity? Whetherthe system itself can detect and report if there is an indica-tion of deception, cheating the system?

II. Problem

A system is the subject of miss management, errors, de-ception, cheating and abuse-general debauchery other. Thecontrol system applied to the information, very useful forthe purpose of maintaining or preventing the occurrenceof things that are not desired [7]. Similarly, Absensi Online(AO), which is part of the RME (Raharja MultimediaEdutainment) require a control that is useful to prevent orkeep things that are negative so that the system will beable to continue to perpetuate his life.Control of both is also very important for web-based infor-mation system to protect themselves from things that hurt,considering the ability of the system to be accessed bymany users, including users who are not responsible [8].One of the ways the system is web-based information sys-tem with the security transparent to the user and does notdisrupt. In this case, whether the behavior detection usingthe concept of data mining can be a choice?Have been described previously that Absensi Online (AO)in the RME functions to serve the lecture. Lecturers cando attendance attendance lecturers and students in AbsensiOnline (AO) in a lecture room in particular. However, theprocess that occurs in the system there is a problem in thecheating-cheating student attendance attendance. This isbecause the system can be read and accessed by all usersconnected to the network so that attendance can be doneby anyone and anywhere.Besides, cheating can also be done by lecturers who doattendance attendance as students’ lecturer states notpresent, the present faculty and students present states,but not present. It is used as indication of deception canalso obtain the results of auditing, meeting lecturers, find-ings, complaints of students and others. When auditing isdone, many in attendance found that Online provides stu-dents present in class when the student outside of class.In addition, attendance at the Online that a class averageof all students present at the appropriate time after thecheck, the lecturers are mengabsen all students at the be-ginning of the first lecture, and if there are students whodo not attend, attendance changes made at the end of thelecture.

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Figure 2. Persentase IMM pertanggal

From figure 2 above, can know the average percentage ofattendance and the number of students each day. If thedata at a time with a very drastic change it can also be anindication of deception, cheating Absensi Online (AO).

Figure 3. Absensi Online (AO) in the lecture

Another thing that can be used as indication of deceptionthat is shown in the image of the circle 3. The student didnot follow the lectures since the beginning of the meeting,but at the meeting to-4 (four) in Absensi Online (AO) tothe student to attend the next meeting and the studentdoes not re-present. It so happens because the studentmay miss out to friends without teachers or attendancedone by the lecturers concerned itself.Other findings are that a number of students obtainingGPA (Komulatif Performance Index) is high when the num-ber of IMM (Quality Index Students) student attendanceis very low, or vice versa. This can also be cheating as anindication.To keep the process on a series of Absensi Online (AO) isrunning properly and the accuracy of the information gen-erated can be guaranteed, it is usually done by way of apassword. However, this action will not cause a user canfind out what information is in addition to the authorizeduser. In addition, the password to be less effective forsystems that are accessed by many users that are alwayschanging from time to time and the activities and preventthe security system does not become transparent to otherusers. Conversely, if there are no access restrictions onincoming Absensi Online (AO), it is possible for the userto perform other deception-deception that is not desired.

From the above description, several problems can be for-mulated as the following:

1. Can we use IT to detect cheating?2. What data mining can be used to it?3. Is there a behavior detection in data mining?

III. Critical Review

A number of critical reviews will be sought for the detec-tion or behavior associated with it. After that the resultswill be considered, sought equality and difference, and todetect weaknesses and strength. Some of the critical re-view are as follows :

1. Data Mining is the exploration and analysis of information valuable or valuable in the possibility of a largedatabase which is used for the purposes of knowledgeand decision-making is better and beneficial for thecompany overall [9].

2. On the different, the data mining is also used for dataor computer security. Techniques of data mining suchas association rules discovery, clustering, deviationdetection, time series analysis, Classification, inductive databases and dependency modeling, and in factmay have been used for fraud or misuse detection.framework that we call anomaly detection system makethis knowledge discovery, but especially to make thesystem more secure to use [10].

3. Research carried out by the data mining experts, Anil KJain in 2000 at Michigan State University on Statisticalpattern recognition approach provides a statistical pattern recognition to the results obtained from data mining. Summarizes this research and compare multiplestages of pattern recognition system to determine howdata mining methods or the most appropriate in thevarious fields, including Classification, clustering, feature extraction, feature selection, error estimation andclassifier combination [11].

4. Research conducted by Wenke Lee in 1998 from Colombia University, the misuse and anomaly detectionby using data mining. Based on the audit data available, the system can be trained to know the behaviorpattern so that the classifier can be mapped using twodimensional axis and mining for procedural disassemblelow frequency pattern. Data mining is described herecan also be applied in this research, so that they canlearn the pattern of previous behavior, and performmapping on the behavior pattern that is now, so it candetect low frequency pattern. When you’ve obtainedthe desired pattern, can be categorized as an anomalylist that you want to be processed further [12].

5. Research is also conducted by Stefanos Manganarisin 1999 at the International Business Machines Corporation examine the context-sensitive anomaly alertsusing real-time Intrusion detections (RTID). The purpose is to alert the entire mengkarakterisasi filtered,

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and compared with historical behavior, so it is usefulto identify the profile of clients is different. The currentresearch can also be equated with this research, processing system because this lecture attendance akanIntrusion that have occurred when the system detectsan anomaly. May not be present but diabsen present.The number of data anomalies that exist, also in thecharacterization, so also has the ability to identify theprofiles of different clients different [13].

6. Research conducted by Mahmood Hossain in 2001 atMississippi State University, almost the same with theresearch conducted by Stefanos Manganaris, data mining framework that is input into the system profile thatadaptive, even using the fuzzy association rule miningto detect the anomaly and normal behavior. Researchthat can be done now also adopted the more so thatprofiling can be adaptive mendeteteksi anomaly [14].

IV. Problem Solving

Basically, the control system Absensi Online (AO) is doneto prevent the deception-deception and negative thingswill happen in the presence of attendance. One of the waysthat you can control is done by using the behavior detec-tion.

However, critical review of the existing note that the focuson examining the behavior detection have not been done,most research conducted to examine the anomaly detec-tion. However, the outline possible anomaly detection canbe useful for the detection behavior as behavior detectioncan be done using the data mining concept.Previously described some of the things that can be usedas an indication of cheating Absensi Online (AO) of whichis the lecturer says students do not attend, the studentattend and present faculty represent the student, the stu-dent does not attend.

The first indication that may not be discussed in this ar-ticle, because if it happens automatically so the studentwill complain to the lecturer concerned. Meanwhile, thesecond indication will be the focus of this aritkel, that ishow the data mining concept, behavior detection can bedone well so that the system itself can know if there is anindication cheating or anomaly.

Next, predective conducted based on data mining andanalysis, and database to build model predicted trendsand information on properties that have not been known[15].Before further discussion, first must be identified on theunusual events that can be represented based on the 7(seven) criteria are as follows :

1. The average student attendanceThe system can detect the cheating by looking at thelevel of attendance of a student. This is the same asthe previous explanation is shown in the picture withthe 3 red circle.

2. Student with low IPK Cumulative Performance Index(IPK) is the average value of the last students duringlectures followed.Students who have an average GPA that low, if at aGPA semester will increase drastically it can also serveas the indication of deception.

3. Data Breach of the previous Breach of previously recorded to have occurred in a database and can be usedas a reference if it happens again.

4. Based on the social background Deception can alsobe seen from the student social background. This isrepresented on the size of the income earned.

5. Friends or gang factor Generally, students have a friendor group (gang) specific. Groups can make a student tobe diligent in following the lectures, or vice versa canalso.

6. Comparing Low Aptitude Test Behavior detection canalso refer to Ujian Saringan Masuk (USM) students, asthe number of GPA and level of attendance a studentmay also be measured from the USM.

7. Comparing IMM Student Quality Index (IMM) is a system that is prepared to measure and know the level ofdiscipline a student attendance by using Online (AO).

Time student attendance during the Teaching LearningActivities (KBM) will be recorded in the whole database.So that it can also serve as the reference behavior detec-tion.

When the system detects an unusual event detection basedon behavior, there are two (2) the possibility that can bedone by the user control system at the time of which is asfollows:

1. Going down to the field and find the truthAbsensi Online (AO) system controllers or admin perform auditing directly to the class that was detected todetermine whether there is a true indication of the existence of cheating or not.

2. Saved a reference behavior for the next detectionBreach that is detected can be recorded and stored inthe database to serve as the reference detection if behavior occurs again Breach.

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Therefore, 7 (seven) the criteria above can be formed intofive (5) itemize the factors described in the following table:

Table 1. Itemize Factor

Before you begin to format your paper, first write and savethe content as a separate text file. Keep your text andgraphic files separate until after the text has been format-ted and styled. Do not use hard tabs, and limit use of hardreturns to only one return at the end of a paragraph. Donot add any kind of pagination anywhere in the paper. Donot number text heads-the template will do that for you.

V. Implementation

Figure 4. Data Mining Architecture Universities Raharja

Before data mining engine is started, the need to centralrepository or data storage that is ideal. Figure 4 above isthe architecture of data mining Universities Raharja. Cannote that the operational data of a role as a source of datais divided into two parts, namely the external data andinternal data. External data is survey data that are usuallyobtained from field studies of students and the Internet.External data is a process that requires continuous andlong to produce data mining reports.

Terms of GPA (see table 1) can be done using the K-meansclustering(). Points. In this article which will be discussedonly two, namely High and Low GPA GPA. To select a

random initial centroids used for the proximity is calcu-lated by using the euclidean distance, that in the end someIterations will be able to find the desired point.

Figure 5. Step-1 in determining the high & low GPAusing the K-means

Figure 6. Last Step in determining high & low GPA usingthe K-means

In the picture 5 and 6 above is the initial step and final stepto determine the clustering of high and low GPA GPA. Canbe K-means Clustering for high and low GPA GPA is asfollows:1. Select K points as the initial GPA centroids2. Repeat3. Form K clusters by assigning all points to the closest

GPA centroid4. Recompute the GPA centroid of each cluster5. Until the GPA centroid don’t change

Absensi Online (AO) system at the University Raharjacan be described using the Unified Modeling Language(UML) are as follows :

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Figure 7. Use Case Diagram Absensi Online (AO)

From the image above can know that there is one systemthat covers all activities Absensi Online (AO) at theUniversity Raharja, two the actor doing the activity as alecturer and lecturer adm, and 6 use the usual case-actoractor is.

Figure 8. Code Generation Sintax Check AO

Figure 8 above is a code generation sintax check use casediagram Abesensi Online (AO). After the test validasinyathat there is no error process. This proved the absence ofany error messages that appear in the Message.Absensi Online at the University Raharja for lecturers andstudents, where a series of attendance begins at the fac-ulty, ie, when the lecturer to use the Check In screen withtouchscreen.

Figure 9. Display Screen Check In Lecturers

Check In after lecturers in a way to emphasize on the thumbthat has been provided on the display screen above, newfaculty can be present in the absent class. Next is the dis-play screen to make lecturers absent in the present class:

Figure 10. Display screen Lecturers To PresentAbsent in Class

When the lecturers have to click on the section be sur-rounded at the top, then this means the process that isabsent a second lecturer in the present class is finished.The process is next to the lecturers to students absent onthe page or screen the same as above.

Figure 11. Display screen Absent Students

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After lecturers do absent present, then automatically linkto absent lecturers will be lost or changed information intothe form of hours to attend lectures in the classroom. Inaddition, the link will display the lecturers to absent stu-dents. When the lecturers have not been absent, the linkwill be written “Absent”, but when the student was ab-sent on the link will change to indicate the number of hoursstudents attended the class, such as 11 appear in the im-age above. The process of absent students can only bedone through the computer where the lecturer concernedto attend absent. At the time this is often an indication ofdeceptions (Breach) as described previously.Figure 11 above is one example an indication of cheatingon attendance attendance Absensi Online (AO). Visible inthe meeting to-1 faculty absent “present” in the classroomat 14:02 and some students attend classes with the sametime. At the meeting to the absent-2 lecturers “present” inthe classroom at 14:08 and a few absent students attendclasses at the same time is at 14:12 but, time information isnot the same as the description of the present faculty inthe classroom. Meeting to-3 present in the classroom lec-tures at 14:09 and the visible presence of student informa-tion in different classes is not the same as the previousmeeting.Meeting to-3 on the image 11 on the normal and visibleindication of the absence of cheating. However, for themeeting to the 1-to-2 and can be used as an indication ofcheating on these lectures. When a lecture is in progressand there are things such as meeting to-1 and-2 to theimage in the top 11, then on the admin computer facultyAbsensi Online (AO) the classroom will look like the fig-ure below.

Figure 12. Views detection in attendance Behavior Online

If the system detects an anomaly, then the data will changeas a red circle in the picture given in the top 12. After adminlecturers see it, the action was admin lecturers can do toauditing the class directly or the data recorded by the sys-tem to be a reference if such things happen again. With thebehavior detection, system can detect and report the caseof the anomaly so that the system performance to be moreoptimal.

VI. Program Listing

To apply the behavior detection using the concept of datamining in attendance Online (AO), one can use the ASPfile. Active Server Pages (ASP) is a script-based serverside, that means the entire application process is doneentirely in the server. ASP file is actually a set of ASPscripts combined with HTML. Thus, the ASP file consistsof several structures that are interconnected and form afunction that returns a. Structure in the ASP file consistsof: text, HTML tags, scripts and ASP [16].

Figure 13. ASP Scripts behavior detectionin Absensi Online

ASP script snippet above is the script used for the detec-tion behavior in attendance Online (AO). Discount scriptto read data on the attendance attendance attendance toinclude in the Online (AO). Anomaly detection occurs ifthe system will immediately provide a report or warning asin the previous 12 figure.

VII. Conclusion

There is a behavior detection system on attendance Online(AO) can minimize or even eliminate cheating-cheating(Breach), which is in the presence of student attendance.Critical review of some existing, it is known that the focuson examining the behavior of detection have not beendone, most research conducted to investigate the anomalydetection. However, outline the anomaly detection can beuseful for the detection behavior for behavior detectioncan be done using the data mining concept. K-means clus-

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tering can be used to segment high GPA and a low GPAcan be used as a reference to detect anomalies.

Behavior detection with the concept of data mining as aform of control system information attendance Online (AO)can be done so with good accuracy and integrity of thedata can be guaranteed and the system can continue toperpetuate his life. In addition, this new concept can stillsupport the main goal of a web-based information system,the display information to all users anytime and anywherewithout the restrictions also access entry.

References

[1]Rahardja, Untung dkk. 2007. SIS: Otomatisasi PelayananAkademik Kepada Mahasiswa Studi Kasus diPerguruan Tinggi Raharja. Jurnal Cyber Raharja.Edisi 7 Th IV/April. Tangerang : Perguruan TinggiRaharja.

[2]Rahardja, Untung dkk. 2008. Presentasi Peluncuran GO(Green Orchestra). Tangerang : Perguruan TinggiRaharja.

[3]Agustin, Lilik. 2008. Design dan Implementasi IntegramPada Perguruan Tinggi Raharja. Skripsi. JurusanSistem Informasi. Tangerang : STMIK Raharja.

[4]Rahardja, Untung dkk. 2007. Raharja MultimediaEdutainment Menunjang Proses Belajar Mengajardi Perguruan Tinggi Raharja. Jurnal Cyber Raharja.Edisi 7 Th IV/April. Tangerang : Perguruan TinggiRaharja.

[5]Rahardja, Untung dkk. 2008. Meng-Capture EQ MelaluiDaftar Nilai Indeks Mutu Komulatif (IMK) BerbasisICT. Jurnal CCIT Vol 1 No.3-Mei. Tangerang :Perguruan Tinggi Raharja.

[6]Rahardja, Untung dkk. 2007. Absensi Online (AO). JurnalCyber Raharja. Edisi 7 Th IV/April. Tangerang:Perguruan Tinggi Raharja.

[7]Hartono, Jogiyanto. 2000. Pengenalan Komputer : DasarIlmu Komputer, Pemrograman, Sistem Informasi danIntellegensia Buatan. Edisi Ke Tiga. Yogyakarta :Andi.

[8]Guritno, Suryo dkk. 2008. Access Restriction sebagaiBentuk Pengamanan dengan Metode IP Token.Jurnal CCIT Vol. 1 No.3-Mei. Tangerang : PerguruanTinggi Raharja.

[9]Han, Jiawei dkk. 2000. DBMiner : A system for data min-ing in relational databases and data warehouses.Data Mining Research Group, Intelligent DatabaseSystems Research Laboratory School of Comput-ing Science. Simon Fraser University : British Co-lumbia.

[10]Chung, Christina. 1998. Applying Data Mining to DataSecurity. University of California : Davis.

[11]Jain, Anil K dkk. 1999. Statistical Pattern Recognition:AReview. IEEE Trans. Department of Computer Sci-ence and Engineering Michigan State University :USA.

[12]Lee, Wenke. Stolfo, Salvatore J. Mok, Kui W. 1998.Mining Audit Data to Build Intrusion DetectionModels. Computer Science Department ColumbiaUniversity : New York.

[13]Manganaris, Stefanos. Christensen, Marvin. Zerkle,Dan. Hermiz, Keith. 1999. A Data Mining Analysisof RTID Alarms. International Business MachinesCorporation (IBM) : USA.

[14]Hossain, Mahmood. Bridges, Susan M. 2001. A Frame-work for an Adaptive Intrusion Detection SystemWith Data Mining. Department of Computer ScienceMississippi State University : USA.

[15]Han, Jiawei dkk. 2000. DBMiner : A system for datamining in relational databases and data warehouses.Data Mining Research Group, Intelligent DatabaseSystems Research Laboratory School of Comput-ing Science. Simon Fraser University : British Co-lumbia.

[16]Bowo, Eko Widodo. 2005 . Membuat Web dengan ASPdan Microsoft Access. Yogyakarta: Andi.

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ABSTRACTIn order to provide assurance about the value of Information Technology (IT), enterprises needs to recognize and

manage benefits and associated risks related to business process and IT. Failures of managing IT can lead to problemsin achieving enterprise objectives as IT is now understood as key elements of enterprise assets.

Control Objectives for Information and Related Technologies (COBIT®) provides good practices across a domain andprocess framework and presents activities in manageable and logical structure. It is known as framework to ensure thatthe enterprise’s IT supports the business objectives by providing controls to measure IT effectiveness and efficiency.COBIT also provides tools for obtaining an objective view of an enterprise’s performance level namely PerformanceIndicator Measurement and Maturity Level Assessment.

In this paper, we propose the implementation of IT Audit process by using COBIT Framework 4.1, the latest COBITversion. Due to the large extent of scope of COBIT Framework 4.1, hereby we delimitate scope of using COBIT’smeasurement tools to determine and monitor the appropriate IT control and performance in the enterprise.

Keywords : COBIT Framework 4.1, IT Audit, Performance Indicator Measurement, Maturity Level Assessment

Saturday, August 8, 200914:20 - 14:40 Room L-211

PERFORMANCE INDICATOR MEASUREMENT AND MATURITYLEVEL ASSESSMENT IN AN IT AUDIT PROCESS

USING COBIT FRAMEWORK 4.1

1. INTRODUCTIONIn 1998, monetary scandal involved Enron and Arthur

Andersen LLP, IT failure at AT & T, and also fund problemson internet and e-commerce development in USA hascaused great growth within IT Audit field.[3]

In that case, the importance of IT Audit really doesmatter to ensure availability and succeed in IT projectsand services inside the company. Framework meansinternational standard that ensure IT Audit process canbe implemented appropriately and meet the requirements,the example of IT Audit Frameworks: COBIT, ITIL, ISO.

2. COBIT FRAMEWORKCOBIT framework used in this paper can be modeledbelow:

The process focus of COBIT illustrated by Figure1subdivides IT into four domains and 34 processes in line

monitor, providing and end-to-end view of IT. [1]of being business-focused, process-oriented, controlsbased, and measurement-driven :

2.1 Business-FocusedCOBIT is focused on business orientation provides

comprehensive guidance for management and businessprocess owner.

To satisfy business objectives, information needs toconform to certain control criteria, COBIT’s informationcriteria are defined as follows [2]:

· Effectiveness· Efficiency· Confidentiality· Integrity· Availability

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Sarwosri, Djiwandou Agung Sudiyono Putro

Department of Informatics, Faculty of Information TechnologyInstitute of Technology Sepuluh Nopember

Gedung Teknik Informatika Lt. 2, Jl. Raya ITS Surabayaemail : [email protected], [email protected]

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· Compliance· Reliability

Every enterprise uses IT to enable business initiatives,and these can be represented as Business Goals for IT.

COBIT also identifies IT Resources as follows:· Applications· Information· Infrastructure· People

2.2 Process-OrientedCOBIT defines IT Activities in a generic process model

within four domains. These domains, as shown in fig.1,are called:

· Plan and Organise (PO) – Provides direction tosolution (AI) and service delivery (DS).

· Acquire and Implement (AI) – Provides thesolutions and passes them to be turned intoservices.

Figure 1. COBIT Framework Model [1]

· Deliver and Support (DS) – Receives thesolutions and makes them usable for end users.

· Monitor and Evaluate (ME) – Monitors allprocesses to ensure that direction provided isfollowed.

2.3 Controls-BasedCOBIT defines control objectives for all 34 processes

as well as overarching process and application controls.Each of COBIT’s IT Process has a process description

and a number of control objectives. The control objectivesare identified by a two-character domain reference (PO,AI, DS, and ME) plus a process number and a controlobjective number.2.4 Measurement-Driven

COBIT deals with the issue of obtaining an objectiveview of an enterprise’s own level to measure where theyare and where improvement is required.

COBIT provides measurement tools as follows:· Maturity Models to enable benchmarking and

identification of necessary capabilityimprovement.

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· Performance goals and metrics, demonstratinghow processes meet business and IT Goalsare used for measuring internal processperformance based on Balanced Scorecardprinciples.

· Activity Goals for enabling effective processperformance.

Measurement tools implemented in this paper will bePerformance Indicator Measurement and Maturity LevelAssessment, as for the implementation scenario of COBITmeasurement tools will be identified by model as shownin figure 2.

3. PERFORMANCE INDICATOR MEASUREMENT

Goals and metrics are defined in COBIT at three levels[1]:

· IT Goals and metrics that define what thebusiness expects from IT and how to measureit.

· Process Goals and metrics that define what theIT process must deliver to support IT’sobjectives and how to measure it.

· Activity Goals and metrics that establish whatneeds to happen inside the process to achievethe required performance and how to measureit.

Goals are defined top-down in that a Business Goal willdetermine a number of IT Goals to support it. Figure 2below provides examples of goal relationships.

we need to define business goals of an enterprise. COBITprovides table linking goals and processes, starting withBusiness Goals to IT Goals, then IT Goals to IT Process.

Goals [1]Enterprise’s representative, called as Auditee,will be asked to define which business goals is conformwith enterprise business goals in Balance Scorecardprinciples.

Auditor, person who conduct IT Audit, give analysisabout audit result, and recommendation along IT Audit

process, will then conduct performance measurement andmaturity models measurement based on IT Goals and ITProcess obtained by following figure 3 and figure 4.

Figure 5. COBIT Table Linking IT Goals to ITProcessesThe terms KGI and KPI, used in previousversion of COBIT, have been replaced with two types ofmetrics:

· Outcome Measures, previously Key GoalIndicators (KGIs), indicate whether the goalshave been met. These can be measured onlyafter the fact, therefore, are called ‘lagindicators’.

· Performance Indicators, previously KeyPerformance Indicators (KPIs), indicate

Figure 2. COBIT Measurement Scenario

Figure 3. Example of COBIT Goal RelationshipsAt first,

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whether goals are likely to be met. They can be measured before the outcome is clear, and therefore, are called‘lead indicators’.

Figure 6 provides possible goal or outcome measures for the example in figure 2.

Figure 4. COBIT Table Linking Business Goals to IT

Figure 5. COBIT Table Linking IT Goals to IT ProcessesThe terms KGI and KPI, used in previous version of COBIT,have been replaced with two types of metrics:

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Auditor needs to do an analysis regarding Outcome Measures and Performance Indicator of the Auditee’s Enterpriseand then giving the Score and Importance appropriate to the Enterprise’s achievement for each COBIT statement.

Measure will be filled with Auditor self opinion regarding Score and Importance, and also facts obtained in the fieldstudy.

4. MATURITY LEVEL ASSESSMENT

COBIT Maturity Models responds to three needs of IT Management for benchmarking and self-assessment tools inresponse to the need to know what to do in an efficient manner:

1. A relative measure of where the enterprise is.

Figure 6. Possible Outcome Measures for the Example in Figure 3.

Figure 7. Possible Performance Drivers for the Example in Figure 3.Outcome Measures and Performance Drivers can be assessed as a process in IT Audit as shown in Figure 8.

Figure 8. COBIT Performance Measurement Table

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2. A manner to efficiently decide where to go.3. A tool for measuring progress against the goal.Maturity modeling for management and control over IT

processes is based on a method of evaluating theorganization, so it can be rated from a maturity level ofnon-existent (0) to optimized (5) as shown in Figure 9.

Figure 9. Graphic Representation of Maturity ModelsUsing the Maturity Models developed for each of

COBIT’s 34 IT processes, management can identify:· The actual performance of the enterprise – Where

the enterprise is today.· The current status of the industry – the

comparison.· The enterprise’s target for improvement – Where

the enterprise wants to be.· The required growth path between ‘as-is’ and

‘to-be’.

Figure 10. COBIT Maturity Level AssessmentTableFigure 10 provides Maturity Level Assessment Tableto assess enterprise’s IT value.

To obtain Value of each statement ‘s answer, we need tomultiply Weight and Answer. With the formula:

(1)

Which V is value score of a maturity statement, W isweight for each maturity statement, and A is the answer’svalue for each maturity statement.

Answer is categorized into four values:1. Not at all with value 0.002. A little with value 0.333. To some degree with value 0.664. Completely with value 1.00

Figure 10. COBIT Maturity Level Assessment TableFigure 10 provides Maturity Level Assessment Table to assessenterprise’s IT value.

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Figure 11. COBIT Maturity Level Calculation

In order to obtain Maturity Level score, first, Auditorneeds to calculate the Compliance for each Maturity Level.

Compliance is obtained by totalize each maturity levelstatement’s value and divide it with total weight for eachstatement.

(2)

Which C is Compliance, V is value for each maturitystatement’s answer, and W is weight for each maturitystatement.

Normalize can be obtained by calculating each level’sCompliance divided with Total Compliance for all level ofan IT Process.

(3)

Which N is Normalize, C is Compliance for each maturitystatement.

Contribution is multiplication of Level with Normalizefor each level. With the total contribution for all level of

an IT Process Auditor will obtain Enterprise’s MaturityLevel Score.

(4)

Which Co is Contribution, L is Level, and N is Normalizefor each maturity level statement.

(5)

Which ML is Maturity Level, and Co is Contributionfor each maturity level statement.

5. CONCLUSION

1. Enterprise will become prescienceregarding of their current IT performancemeasured by COBIT Performance IndicatorMeasurement.

2. COBIT Framework can be used as thesource of recommendation for increasingenterprise’s IT performance.

3. With a view of Maturity LevelAssessment result, enterprise will be able tospecify its IT strategy in alignment withBusiness Strategy.

4. Escalation of enterprise’s maturity levelis enabled by remarking recommendationsmade using COBIT Framework.

REFERENCES

[1] IT Governance Institute.2007.COBIT 4.1[2] Indrajit, Richardus Eko. Kajian Strategis

Analisa Cost-Benefit Investasi TeknologiInformasi.

[3] NationMaster.com. 2008. History of InformationTechnology Auditing<URL : http://www.nationmaster.com/encyclopedia/History-of-i n f o r m a t i o n - t e c h n o l o g y -auditing.htm#Major_Events>

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Paper

Informatics Department, Faculty of Information TechnologyInstitut Teknologi Sepuluh Nopember, Kampus ITS Surabaya

Email: [email protected], [email protected]

ABSTRACTThis paper presents an experimental study on bank performance prediction base on financial report. This research useSupport Vector Machine (SVM), Probabilistic Neural Network (PNN) and Radial Basis Function Neural Network(RBFN) methods to experiment the bank performance prediction. To improve accuracy prediction of both neuralnetwork methods, this research use Principal Component Analysis (PCA) to get best feature. This research work basedon the bank’s financial report and financial variables predictions of several banks that registered in Bank Indonesia.The experimental results show that the accuracy rate of bank performance prediction of PCA-PNN or PCA-RBFNmethods are higher than SVM method for Bank Persero, Bank Non Devisa and Bank Asing categories. But, the accuracyrate of SVM method is higher than PCA-PNN or PCA-RBFN methods for Bank Pembangunan Daerah and Bank Devisacategories. The accuracy rate of PCA-PNN method for all bank categories is comparable to that PCA-RBFN method.

Keywords: bank performance prediction, support vector machine, principal component analysis, probabilistic neuralnetwork, radial basis function neural network

Saturday, August 8, 200914:45 - 15:05 Room L-210

AN EXPERIMENTAL STUDY ON BANK PERFORMANCE PREDICTIONBASE ON FINANCIAL REPORT

1. INTRODUCTIONThe prediction of accuracy financial bank has been

the extensively researched area since late. Creditors,auditors, stockholders and senior management are allinterested in bankruptcy prediction because it affects allof them alike [7].

When the shareholders will make the investmentto a bank, the shareholder must first see the performanceof banks is good or not [2]. In some cases accuratelypredicted the performance of a bank can also througheconomic and financial ratio, the current assets / totalassets, current assets - cash / total assets, current assets/ loans, reserve / loans, net income / total assets, netincome / total capital share, net income / loans, cost ofsales / sales, cash flow / loan.

Some research [3] use neural network approach forperformance predictions, neural network considered asan alternative network to predict accuracy that can resultin the total value of the error more or less the same ErrorType 1 and Error Type 2.

Type of error is determined from the predictedperformance of the bank. Error Type 1 as the number of“actually poor performance banks” predicted as“adequate performance banks” expressed as percentageof total poor performance banks and Error Type 2 as thenumber of “actually adequate performance banks”predicted as “poor performance banks” expressed as apercentage of total adequate performance banks.

Ryu and yue researchers [9] introduce isotonikto predict the spread of a financial company and produceMLFF-BP, logistic regression, and probit methods. Byusing the data from one of a financial company, predictedthe failure of small community banks and regional banksor big banks use MLFF-BP, MDA, and professionalassessment. For community banks and regional banks,the researchers observed that the neural network modelproduce MDA model, especially type I error. The result ispredicted in the small community bank is less accuratethan the regional banks.

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Chastine Fatichah, Nurina Indah Kemalasari

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The objective of the this study is primarily toexperiment several method of soft computing to analysisError Type 1 and Error Type 2 of bank performanceprediction.

The rest of the paper is organized as follows;Section 2 describes methods of bank performanceprediction such as SVM, PNN, and RBFN methods. Section3 describes experimental result and evaluationperformance, and Section 4 describes conclusion of thisresearch.

2. METHODS OF BANK PERFORMANCEPREDICTION

This section presents methods of bank performanceprediction that used in this research. This research useSupport Vector Machine (SVM), Probabilistic NeuralNetwork (PNN) and Radial Basis Function Neural Network(RBFN) methods. To improve accuracy prediction of bothneural network methods, this research use PrincipalComponent Analysis (PCA) to reduce the dimension ofthe input space. Each of the constituent methods is brieflydiscussed below.

2.1 Support Vector MachineSupport Vector Machine (SVM) [1] is a method for

obtaining the optimal boundary of two sets in a vectorspace independently on the probabilistic distributions oftraining vectors. Its fundamental idea is locating theboundary that is most distant from the vectors nearest tothe boundary in both of the sets. Note that the optimalboundary should classify not only the training vectors,but also unknown vectors in each set. Although thedistribution of each set is unknown, this boundary isexpected to be the optimal classification of the sets, sincethis boundary is the most isolated one from both of thesets. The training vectors closest to the boundary arecalled support vectors. Figure 1 illustrates the optimalboundary by SVM method.

˜˜˜˜˜¿¿¿¿¿¿optimal boundary

support vectors

Figure 1. Optimal Boundary by SV M method

The optimal boundary is computed as decision surface ofthe form:

))(sgn()( xgxf = (1)where,

(2)

In Equation 2, K is one of many possible kernelfunctions, { }1,1−∈iy is the class label of the data

point *ix , and { }*

1* l

iix = is subset of the training data set.

*ix are called support vectors and are the points from

the data set that fall closest to the separating hyper

plane. Finally, the coefficients iα and b are determinedby solving a large-scale quadratic programmingproblem. The kernel function K that is used in thecomponent classifier is a quadratic polynomial and hasthe form shown below:

2** )1.(),( += ii xxxxK (3)

{ }1,1)( −∈xf in equation (1) is referred to as the binaryclass of the data point x which is being classified by theSVM. Values of 1 and -1 refer to the classes of positiveand the negative training examples respectively. AsEquation (1) shows, the binary class of a data point is thesign of the raw output g(x) of the SVM classifier.

The raw output of a SVM classifier is the distanceof a data point from the decision hyper plane. In general,the greater the magnitude of the raw output, the morelikely a classified data point belongs to the binary class itis grouped into by the SVM classifier.

2.2 Principal Component AnalysisPrincipal component analysis (PCA) [5] has been

called one of the most valuable results from applied linearalgebra. PCA is used abundantly in all forms of analysis -from neuroscience to computer graphics - because it is asimple, non-parametric method of extracting relevantinformation from confusing data sets. With minimaladditional effort PCA provides a roadmap for how to reducea complex data set to a lower dimension to reveal the

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sometimes hidden, simplified structure that often underlieit.

2.3 Probabilistic Neural NetworkThe PNN [4] employs Bayesian decision-making

theory based on an estimate of the probability density inthe data space and Parzen estimates to make predictions.PNN requires onepass training and hence learning is veryfast. However, the PNN works for problems with integeroutputs, hence, can be used for classification problems.PNN is not stuck in some local minima of the error surface.The PNN as implemented here has 54 neurons in the inputlayer corresponding to the 54 input variables in thedataset. The pattern layer stores all the training patternsone in each pattern neuron. The summation layer has twoneurons with one neuron catering to the numerator andanother to the denominator of the non-parametricregression estimate of Parzen. Finally, the output layerhas one neuron indicating the class code of the pattern.

2.4 Radial Basis FunctionRBFN [4], another member of the feed-forward

neural networks, has both unsupervised and supervisedtraining phases. In the unsupervised phase, the input dataare clustered and cluster details are sent to the hiddenneurons, where radial basis functions of the inputs arecomputed by making use of the center and the standarddeviation of the clusters. The radial basis functions aresimilar to kernel functions in kernel regression. Theactivation function or the kernel function can assume avariety of functions, though Gaussian radial basisfunctions are the most commonly used. The learningbetween hidden layer and output layer is of supervisedlearning type where ordinary least squares technique isused. As a consequence, the weights of the connectionsbetween the kernel layer (also called hidden layer) andthe output layer are determined. Thus, it comprises ahybrid of unsupervised an supervised learning. RBFN cansolve many complex prediction problems with quitesatisfying performance.

3. EXPERIMENTAL RESULT ANDANALYSIS PERFORMANCE

The first process of this system is collect financialreport of banks that registered in Bank Indonesia about110 banks. Data are taken as the period of 1 year, so thatthe amount of data used for 1320 data. From the amountof data, divided into 660 data for training data, and 660data for testing data. There are six bank categories thatused to evaluate bank performance prediction i.e. BankPembangunan Daerah, Bank Persero, Bank Devisa, BankNon Devisa and Bank Asing.

Data are taken based on the variables from thefinancial report below:

1. Earning asset2. Total loans3. Core deposit4. Non-interest5. Interest income6. Gain(losses)7. Non-interest expense-wages and salary8. Total interest expense9. Provision expense10. Off balance sheet commitment11. Obligation and letter of credit

The second process of this system identifies bankfinancial variables that would be used to classify data.There are two bank financial variables such good andpoor variables.

The third process of this system is bankperformance prediction using SVM, PCA-PNN, and PCA-RBFN methods to produce Error Type 1, Error Type 2 andaccuracy. To evaluate Error Type 1, Error Type 2 andaccuracy for each method used testing data of each bankcategory. PCA method is used to get best feature ofdataset before is classified by PNN or RBFN. Base onseveral references that PCA is one of the best methodsfor reducing attribute of dataset but not lost importantinformation of data.

The experiment results of one bank sample of BankPembagungan Daerah categories on 1 year (Table 1) showthat Error Type 1 of PCA-RBFN method is lowest and theaccuracy of SVM method is highest.

The experiment results of one bank sample of BankPersero categories on 1 year (Table 2) show that ErrorType 1 of SVM method is highest and the accuracy ofSVM method is lowest.

The experiment results of one bank sample of BankDevisa categories on 1 year (Table 3) show that theaccuracy of SVM method is highest.

The experiment results of one bank sample of BankNon Devisa categories on 1 year (Table 4) show that ErrorType 1 of SVM method is highest and the accuracy ofSVM method is lowest.

The experiment results of one bank sample of BankAsing categories on 1 year (Table 5) show that theaccuracy of SVM method is lowest.

The experimental results show that the accuracyrate of bank performance prediction of PCA-PNN or PCA-RBFN methods are higher than SVM method for BankPersero, Bank Non Devisa and Bank Asing categories.But, the accuracy rate of SVM method is higher than PCA-PNN or PCA-RBFN methods for Bank PembangunanDaerah and Bank Devisa categories. The accuracy rate ofPCA-PNN method for all bank categories is comparableto that PCA-RBFN method.

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Tabel 1. The result of Bank Pembangunan Daerahcategory

Methods Error and Accuracy namePercentages of error and accuracy

SVM Error Type 1Error Type 2Accuracy16.67%8.33%75%

PCA-RBFN Error Type 1Error Type 2Accuracy0%33.33%66.67.%

PCA-PNN Error Type 1Error Type 2Accuracy25%8.33%66.67.%

Table 2. The result of Bank Persero category

Methods Error and Accuracy name Percentages oferror and accuracy

SVM Error Type 1Error Type 2Accuracy25%16.67%58.33%

PCA-RBFN Error Type 1Error Type 2Accuracy0%16.67%83.33%

PCA-PNN Error Type 1Error Type 2Accuracy0%16.67%83.33%

Table 3. The result of two bank sample of Bank Devisacategory

Methods Error and Accuracy namePercentages of error and accuracy

SVM Error Type 1Error Type2Accuracy 16.67%0%83.33%

PCA-RBFN Error Type 1Error Type2Accuracy 0%25%75%

PCA-PNN Error Type 1Error Type2Accuracy 16.67%8.33%75%

Table 4. The result of two bank sample of Bank NonDevisa category

Methods Error and Accuracy namePercentages of error and accuracy

SVM Error Type 1Error Type 2Accuracy16.67%16.67%66.67%PCA-RBFN Error Type 1Error Type 2Accuracy0%16.67%83.33%PCA-PNN Error Type 1Error Type 2Accuracy0%16.67%83.33%

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Table 5. The result of two bank sample of Bank Asingcategory

Methods Error and Accuracy namePercentages of error and accuracy

SVM Error Type 1Error Type 2Accuracy8.33%16.66%75%

PCA-RBFN Error Type 1Error Type 2Accuracy16.66%0%83.3333%

PCA-PNN Error Type 1Error Type 2Accuracy0%16.66%83.3333%

4. CONCLUSION

This paper presents bank performance predictioncan be used to evaluate bank performance in real cases.

5. REFERENCE

[1]. A. Asano, “ Pattern information processing”,

Session 12 (05. 1. 21), 2004.

[2]. “Bank”.(http://en.wikipedia.org/wiki/Bank).

[3]. K.Y. Tam, M. Kiang, Predicting bank failures: a

neural network approach, Decis. Sci. 23 (1992)

926–947.

[4]. Simon Haykin, Neural Network: A Comprehensive

Foundation.(2005) 240-301.

[5]. Smith, Lindsay I.26 Pebruari 2006.”A Tutorial on

Principal Component Analysis”.

[6]. Support Vector Machines, available at http:/

www.svms.org/introduction.html; libSVM tool can

be downloaded from http://www.csie.ntu.edu.tw/

cjlin/libsvm.

[7]. V.Ravi, H.Kurniawan, Peter Nwee Kok Thai, dan

P.Ravi Kumar, “Soft computing system for bank

performance prediction”, IEEE Journal, February

2007.

[8]. Y.U. Ryu, W.T. Yue, Firm bankruptcy prediction:

experimental comparison of isotonic separation and

other classification approaches, IEEE Trans. Syst.,

Manage. Cyber.—Part A: Syst. Hum. 35 (5) (2005)

727–737

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Paper

AbstractMany research has been done on information technology planning effectiveness in a developing country, this paper takesa step further in examining such factors in Indonesia, which is also a developing country. The results have surprisinglyshown that empirical data produced in Indonesia is not consistent to those researches conducted in other developingcountries. Hence we come to conclude that a study of one developing country on E-laerning Effectiveness cannot andshould not represent all developing nations in the world. One should carefully study the regional cultures and back-ground that will eventually help to determine one different IT behaviors to another. For research to become effective,hypothesis should be tested on several different countries and then follow by paying attention on similar behavior on theresults before drawing the conclusion. Based on some previous research about IT Planning Effectiveness in a DevelopingCountry, we perform similar research in Indonesia on the 6 hypothesis research model previously performed in Kuwait[1].

Index Terms — Information Technology Planning Effectiveness, Developing countries, Indonesia

Saturday, August 8, 200913:30 - 13:50 Room L-212

Critical Success Factor of E-Learning Effectivinessin a Developing Country

I. IntroductionThe term Critical Success Factor (CSF) is defined simplyas “The thing(s) an organization MUST do to be success-ful.” [2]. This definition is translated into the conceptualcontext of our subject, which is the critical success factorof IT planning effectiveness. What are the thing(s) a de-veloped country MUST have in order to be effective in E-laerning? As a whole, good E-laerning must be able tointegrate the business perspectives of the other organiza-tional functions into an enterprise IT perspective that ad-dresses strategic and internal technology requirements.Research on Information Technology planning effective-ness has been done to many developing countries, such

as North America [3], Latin America [4], Western Europe[5], Eastern Europe [6], South East Asia [7], and the MiddleEast. However, no similar research has been done on infor-mation technology planning effectiveness in Indonesia.Observing previous research shows little correlation be-tween similar researches conducted on different regionalparts of the world. We strongly believe that regional cul-tures and behaviors affect final results in the study ofrelationships of factors relating to information technologyplanning effectiveness.Past research concludes that informed IT management,management involvement, and government policed con-

Untung RahardjaSTMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

Jazi Eko IstiyantoGADJAH MADA UNIVERSITY

Yogyakarta, Republic of [email protected]

Sri DarmayantiSTMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

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tributes most to IT planning effectiveness. However, ITpenetration, user involvement and financial resources doeslittle effect to IT planning effectiveness [8]. We take a stepforward by conducting similar research in Indonesia, toprove whether those hypotheses are true. The article isdivided into five sections. Section 1 is the abstract andthe introduction. Section 2 describes the research modeland prediction of theory. Section 3 describes comparisonof results. Section 4 discusses the global implications ofour findings and the conclusion of our paper. Section 5 isthe limitations and future directions.

Figure 1. The Research Model

I. Research Model and Prediction of TheoryThis section restates the research model used by Aladwaniin conducting study in Kuwait [9]. Looking at the strate-gic alignment model perspective [10], given a concise pic-ture of business strategy which is the external domain, E-laerning effectiveness should originate from the internaldomains of IT. Particularly, Aladwani look at the organiza-tional point of view affecting E-laerning effectiveness.We would like to point out that the three organizationalfactors affecting E-laerning effectiveness is not thorough,let alone adequate in representing organizational behav-iors. Factors such as administrative structure and presentcritical business processes should also be considered.Furthermore, the environmental factors, which seems tobe further extended from the organizational internal model,should include additional factors besides governmentpolicies. Factors such as nations’ economic strength, so-cial and cultural value certainly contributes in determin-ing the environmental factors of E-laerning effectiveness.For the purpose of this discussion, let us focus on the sixfactors affecting E-laerning effectiveness, which is pre-sented by Aladwani in the form of hypotheses. The re-search model in systematically represented in Figure 1.

A. Information Technology Factor

Information technology penetration may be conceptual-ized as the extent to which information technology is avail-able throughout the premises of the organization [11]. Pre-

vious IS research in developed countries has shown thatincreasing information technology penetration into an or-ganization leads to favorable business consequences [12].The benefits of higher IT penetration include support forthe linkage of IT-business plans and evaluation and re-view of the IT strategy [13]. However, only one existingresearch has specifically examined the relationship betweeninformation technology penetration and planning effec-tiveness in developing countries which is done byAladwani. Therefore, it is not clear yet whether informa-tion technology penetration would affect information tech-nology planning effectiveness in the context of a develop-ing country such as Indonesia. The aim of the presentstudy is to continue the previous attempt to test the fol-lowing prediction:

H1: Information technology penetration is one of the criti-cal success factors of information technology planningeffectiveness in developing countries.

Prediction of theory on H1:

Based on unofficial survey among friends, colleagues andfamily, also based on personal experience, we mostly agreethat information technology penetration will have a sub-stantial positive effect on information technology plan-ning effectiveness. As we humbly look at the regional data,metropolitan city such as Jakarta, which have the highestinformation technology penetration, have the best tech-nology infrastructures and services. On the contrary, sub-urban and countryside areas, which have the low informa-tion technology penetration, are also low on technologybased facility. Thus we assume that organizations in thebig city do better technology planning effectiveness com-pare to the countryside areas. Hence information technol-ogy penetration will have a positive effect on informationtechnology planning effectiveness in developing countrysuch as Indonesia.

B. Organizational Factor

In this study, organizational factors were measured usinginformed information technology management, manage-ment involvement, user involvement, and adequacy of fi-nancial resources for information technology planning.Informed information technology management in this studyrefers to the extent to which information technology man-agement is informed about organizational goals and plans[14]. There is a consensus among researchers that the ef-fectiveness of information technology planning is depen-dent on its integration with business objectives [15]. WeakIT-business relation was found to be among the top keyissues facing information technology managers in Asian

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countries. Informed information technology managementhelps to accomplish certain information technology plan-ning objectives such as better utilization of informationtechnology to meet organizational objectives. Thus, wehypothesize that:

H2: Well informed IT management is one of the criti-cal success factors of information technology planningeffectiveness in developing countries.

Prediction theory on H2:

This is thus true whether this hypothesis is tested on de-veloped country or developing country, that obviouslyinformed IT management have a positive effect on infor-mation technology planning effectiveness. Inversely, un-informed IT management has adverse effect on IT plan-ning because IT management is involved directly to E-laerning. Hence, for E-laerning to be effective, IT man-agement has to be well informed on technology skills andupdate necessary. Thus, we fully support hypothesis H2to be true that the critical success factor of E-laerningeffectiveness in developing country such as Indonesia iswell informed IT management.Management involvement is defined as the extent to whichmanagement is involved in the planning process [16],whereas user involvement is defined as the extent to whichthere is adequate user involvement in the planning pro-cess [17]. Both management involvement and user involve-ment are important ingredients for successful informationtechnology planning [18];[19]. Premkumar and King re-ported higher management involvement in strategic IS plan-ning [20]. Gottschalk reported a positive relationship be-tween user involvement and the effective implementationof information technology planning [21]. Gibson assertedthat management involvement is critical for successful plan-ning for information technology transfer to Latin Ameri-can countries [22]. In a study of information technologyprojects in Kuwait, Aladwani emphasized the importanceof involving management and users in information tech-nology implementation activities [23]. Thus, in order toconduct similar survey in another developing country suchas Indonesia, we hypothesize that:

H3: Management involvement is one of the criticalsuccess factors of information technology planning effec-tiveness in developing countries.

Prediction theory on H3:

Management is part of the stakeholder of information tech-nology development. It also means that they are the sourceto provide funding to conduct operation such as E-laerning. Funding becomes available after the manage-ment approval of E-laerning. Hence the management has

to be involved in information technology planning, if wewant proper resource and funding to support the IT build-ing. Hence we support the hypothesis that managementinvolvement is definitely one of the critical success fac-tors of information technology planning effectiveness indeveloping countries such as Indonesia.H4: User involvement is one of the critical success factorsof information technology planning effectiveness in de-veloping countries.

Prediction theory on H4:

Organizations in developing countries too [24] If organi-zations want to benefit from information technology plan-ning, then they must allocate adequate financial resourcesfor information technology planning [25]. Thus, we hy-pothesize that:

H5: Adequacy of financial resources is one of the criticalsuccess factors of information technology planning effec-tiveness in developing countries.

Prediction theory on H5:

As it appear true that the extent of adequate financial avail-ability mirror the success of information technology plan-ning effectiveness in developed countries [26], it is evenmore critical for the financial factor to be available ad-equately for information technology planning to be effec-tive in developing countries. Where E-laerning in devel-oping countries normally correspond to extensive deploy-ment of new IT infrastructure, it is obvious that large fund-ing is needed to purchase the infrastructure and to hirehuman resources, starting at the earliest stage such asplanning activities. Hence, we fully support that adequatefinancial resources is one of the critical success factor ofinformation technology planning effectiveness in devel-oping countries such as Indonesia.

A. Environmental Factor

The environment of the IS organization is a critical deter-minant of its performance [27]. One of the major dimen-sions of the external environment is government policy,which is defined as the extent to which top managementviews government policies to be restrictive of liberal [28].Understanding information technology management is-sues such as information technology planning in a globalsetting would require examining government policies inthe local country [29]. In Saudi Arabia, Abdu-Gader andAlangari [30] reported that government practices and poli-cies were among the top barriers of information technol-ogy assimilation. Organizations viewing government poli-cies to be restrictive are expected to have less computer-ization [31];[32]; and are expected to devote less attention

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to strategic IT related activities such as information tech-nology planning [33]. As it appears that the hypothesisenvironmental factors such as government policies aresupported in Kuwait [34], we continue the same hypoth-esize that:

H6: Liberal government policy is one of the critical suc-cess factors of information technology planning effective-ness in developing countries.

Prediction theory on H6:

Liberal government policies generally act as a catalyst toencourage existing organization to extensively perform E-laerning. How effective is the performance of technologyplanning somewhat contributes little for this purpose. Weconclude that a liberal government policy is none whatso-ever contribute to the effectiveness of E-laerning. Hencea liberal government policy is not one of the critical suc-cess factors of information technology planning effective-ness in developing countries such as Indonesia.

II. Comparison of Result

After we conduct similar research on E-laerning effec-tiveness in developing countries such as Indonesia, aspreviously conducted in another developing countriesKuwait, we would like to produce a table of comparisonbetween the two results.

Table 1. Comparison of Results between Kuwait and Indo-nesia

We observe that the research countries being conductedare both developing countries. They do not however pro-duce the same results. Three of the hypotheses that isInformed IT Management, Management Involvement andUser Involvement are supported similarly for both devel-oping countries, whereas three other hypotheses that isIT penetration, financial resources, and government poli-cies are supported differently for both developing coun-tries.

I. Global Implication and ConclusionThe goal of this study was to explicate the nature of con-textual correlates of information technology planning ef-fectiveness in Indonesia, in comparison to Kuwait. The

present investigation contributes to the literature by be-ing one of the first studies to provide an empirical test ofinformation technology planning effectiveness in the con-text of developing countries. Our analysis for both coun-tries reveals mixed and different support for the proposedrelationships. In accordance with the findings of informa-tion technology planning research in Indonesia, we founda positive relationship between IT penetration, manage-ment involvement, informed information technology man-agement, and financial resources. On the other hand, wefound no support for a positive relationship between userinvolvement and liberal government policies on determin-ing the critical success factor of information technologyplanning effectiveness.

Management involvement is found to have a positive rela-tionship with information technology planning effective-ness. This result is somewhat expected. This finding con-firms one more time the importance of management involve-ment in information technology initiatives in contempo-rary organizations. It is not surprising as it is pointed ear-lier in the paper that management involvement is more sub-stantial in developing countries compare to developedcountry as the critical success factor of E-laerning effec-tiveness. This finding indicates that management involve-ment is the most important facilitator of information tech-nology planning in the research model. It coincides withour findings that both countries Kuwait and Indonesiaboth supports Management Involvement hypothesis (H3).Informed information technology management is the sec-ond most significant correlate of information technologyplanning effectiveness in our study (H2). As we can see inthe comparison tables, that Indonesia and Kuwait, bothsupport the hypothesis. The findings show that an in-formed information technology manager plays an impor-tant role in enhancing information technology planningeffectiveness through improving communication with topmanagement of the organization. Additionally, the findingalso show that an informed information technology man-ager has a greater propensity to develop work plans thatsupport organizational goals and activities leading to bet-ter integration of IT-business plans.

The findings of past information technology planning re-search highlight the importance of informed informationtechnology management for organizations operating indeveloped countries [35] and our finding highlights thesimilar findings of the importance for organizations oper-ating in developing countries as well.

Furthermore, we found contrasting relationship on liberalgovernment policies and information technology planningeffectiveness. Extensive government liberalization has beenconducted in Indonesia where e-government plan sup-ported fully by ICT, Nusantara 21, SISFONAS and

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BAPPENAS has been seriously conducted [36]. Yet de-spite such effort, E-laerning effectiveness is still very mini-mal. This finding is not consistent with our theorizing andwith the findings of Dasgupta and his colleagues [37];[38]. Even though liberal government policies was rankedthe second most significant determinant of informationtechnology planning effectiveness in the ITPE-5 model(IT for gaining competitive advantage), survey conductedon the two countries failed to both support the hypoth-esis. When organizations perceive government policies tobe less restricting, they become more inclined to engage inoperations aimed at exploiting information technologyopportunities for gaining competitive advantage.

II. Limitations and Future Directions

Hofstede suggests that there are differences between de-veloped and developing countries along these culturalaspects. Contrasting the culture of Kuwait and the UnitedStates gives a good example of Hofstede’s scheme [39].However, not mentioning the developed countries, are thereany differences among the developing countries alongthese cultural aspects? Are there any similarity of E-laerning effectiveness results in developing countrieswhich have regional proximity, share the same religions,climates, and cultures? On the other hand, are there anysimilarity of E-laerning effectiveness results in develop-ing countries which are distant, do not share the samereligions, climates and cultures. We will not be surprisedto see that different provinces or states on the same devel-oping country will produce different empirical results. Wesuggest that further research should starts on bettergrouping of data field rather than grouping by developedor developing countries to come to conclusion on factorsaffecting E-laerning effectiveness.

References

[1]Aladwani, A.M. (2000). IS project characteristics andperformance: A Kuwaiti illustration. Journal of Glo-bal Information Management, Vol.8 No.2 , pp. 50-57.

[2]Luftman, J. (1996). Competing in the Information Age –Strategic Alignmen in Practice, ed. By J. Luftman.Oxford University Press.

[3]Brancheau, J.C., Janz, B.D & Wetherbe J.C. (1996). KeyIssues in Information Systems Management: 1994 –95 SIM Delphi Results. MIS Quarterly, Vol. 20 No. 2,pp. 225-242.

[4]Mata, F.J. & Fuerst, W.L. (1997). Information SystemsManagement Issues in Central America: A Multina-tional and Comparative Study. Journal of Strategic

Information Systems, Vol. 6. No. 3, pp. 173-202.

[5]Gottschalk, P (1999). Global comparisons in key issuesin IS Management: Extending Initial Selection Proce-dures and an Empirical Study in Norway. Journal ofGlobal Information Technology Management, Vol. 6No. 2, pp. 35-42.

[6]Dekleva, S.M., & Zupanzic, J. (1996). Key Issues in In-formation Systems Management: A Delphi Study inSlovenia. Information & Management, Vol. 31 No. 1,pp. 1-11.

[7]Moores, T.T (1996). Key Issues in the Management ofInformation Systems: A Hong Kong Perspective. In-formation & Management, Vol. 30 No. 6, pp. 301- 307.

[8]Aladwani, A.M. (2001). E-laerning Effectiveness in aDevelopment Country. Journal of Global InformationTechnology Management, Vol.4 No.3, pp. 51-65.

[9]Aladwani, A.M. (2001). E-laerning Effectiveness in aDevelopment Country. Journal of Global InformationTechnology Management, Vol.4 No.3, pp. 51-65.

[10]Henderson, J.C. & Venkatraman, N. (1999). StrategicAlignment: Leveraging information technology fortransoforming organizations. IBM System Journal,Vol.32 No.1, pp.472-484.

[11]Benbasat, I., Dexter, A.S., & Mantha, R.W. (1980). Im-pact of organizational maturity of information sys-tem skill needs. MIS Quarterly, Vol. 4 No. 1, pp. 21-34.

[12]Winston, E.R., & Dologite, D.G (1999). Achieving ITInfusion: A Conceptual Model for Small Businesses.Information Resources Management Journal, Vol. 12No. 1, pp. 26-38.

[13]Cerpa, N. & Verner, J.M (1998). Case Study: The effectof IS Maturity on Information Systems Strategic Plan-ning. Information & Management, Vol. 34 No. 4, pp.199-208.

[14]Premkumar, G. & King, W.R (1992). An Empirical As-sessment of Information Systems Planning and theRole of Information Systems in Organizations. Jour-nal of Management Information Systems, Vol. 9 No.2, pp. 99-125.

[15]Cerpa, N. & Verner, J.M (1998). Case Study: The effectof IS Maturity on Information Systems Strategic Plan-ning. Information & Management, Vol. 34 No. 4, pp.

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199-208.

[16]Premkumar, G. & King, W.R (1992). An Empirical As-sessment of Information Systems Planning and theRole of Information Systems in Organizations. Jour-nal of Management Information Systems, Vol. 9 No.2, pp. 99-125.

[17]Premkumar, G. & King, W.R (1992). An Empirical As-sessment of Information Systems Planning and theRole of Information Systems in Organizations. Jour-nal of Management Information Systems, Vol. 9 No.2, pp. 99-125.

[18]Lederer, A.L. & Mendelow, A.L. (1990). The Impact ofthe Environment on the Management of InformationSystems. Information Systems Research, Vol.1 No. 2,pp. 205-222.

[19]Lederer, A.L. & Sethi, V. (1988). The Implementaion ofStrategic Information Systems Planning, DecisionSciences, Vol. 22 No. 1, pp. 104-119.

[20]Premkumar, G. & King, W.R (1992). An Empirical As-sessment of Information Systems Planning and theRole of Information Systems in Organizations. Jour-nal of Management Information Systems, Vol. 9 No.2, pp. 99-125.

[21]Gottschalk, P. (1999). Strategic Information SystemsPlanning: The IT Strategy Implementation Matrix.European Journal of Information Systems, Vol. 8 No.2, pp. 107-118.

[22]Gibson, R. (1998). Informatics Diffusion in South Ameri-can Developing Economies. Journal of Global Infor-mation Management, Vol. 6 No. 1, pp. 35-42.

[23]Aladwani, A.M. (2000). IS project characteristics andperformance: A Kuwaiti illustration. Journal of Glo-bal Information Management, Vol.8 No.2 , pp. 50-57.

[24]Abdul-Gader, A.H., & Alangari, K.H. (1996). Enhanc-ing IT assimilation in Saudi public organizations:Human resource issues. In E. Szewczak & M.Khosrowpour (Eds.), The human side of informationtechnology management, Idea Group Publishing, pp.112-141.

[25]King, W.R (1978). Strategic Planning for ManagementInformation Systems. MIS Quarterly, Vol. 2 No.1, pp.26-37.

[26]Lederer, A.L. & Sethi, V. (1988). The Implementaion ofStrategic Information Systems Planning, DecisionSciences, Vol. 22 No. 1, pp. 104-119.

[27]Lederer, A.L. & Mendelow, A.L. (1990). The Impact ofthe Environment on the Management of InformationSystems. Information Systems Research, Vol.1 No. 2,pp. 205-222.

[28]Dasgupta, S., Agarwal, D., Ioannidis, A. &Gopalakrishnan, S. (1999). Determinants of Informa-tion Technology Adoption: An extension of existingmodels to firms in a Developing Country. Journal ofGlobal Information Management, Vol. 7 No. 3, pp. 30-40.

[29]Watad, M.M (1999). The Context of Introducing IT/IS-based Innovation into Local Government in Colom-bia. Journal of Global Information Management, Vol.7 No. 1, pp. 39-45.

[30] Abdul-Gader, A.H., & Alangari, K.H. (1996). En-hancing IT assimilation in Saudi public organizations:Human resource issues. In E. Szewczak & M.Khosrowpour (Eds.), The human side of informationtechnology management, Idea Group Publishing, pp.112-141.

[31]Dasgupta, S., Ionnidis, A., & Agarwal, D. (2000). Infor-mation Technology Adoption in the Greek BankingIndustry. Journal of Global Information TechnologyManagement, Vol. 3 No. 3, pp. 32-51.

[32]Dasgupta, S., Agarwal, D., Ioannidis, A. &Gopalakrishnan, S. (1999). Determinants of Informa-tion Technology Adoption: An extension of existingmodels to firms in a Developing Country. Journal ofGlobal Information Management, Vol. 7 No. 3, pp. 30-40.

[33]Palvia, S.C., & Hunter, M.G (1996). Information Sys-tems Development: A Conceptual Model and a Com-parison of Methods used in Singapore, USA andEurope. Journal of Global Information Management,Vol. 4 No. 3, pp. 5-16.

[34]Aladwani, A.M. (2001). E-laerning Effectiveness in aDevelopment Country. Journal of Global InformationTechnology Management, Vol.4 No.3, pp. 51-65.

[35]Premkumar, G. & King, W.R (1992). An Empirical As-sessment of Information Systems Planning and theRole of Information Systems in Organizations. Jour-nal of Management Information Systems, Vol. 9 No.2, pp. 99-125.

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[36]Rusli, A. & Salahuddin, R. (2003). E-Government Plan-ning in Indonesia: A Reflection against Strategic In-formation Communication Technology Planning Ap-proaches. Proceedings for the Kongres IlmuPengetahuan Nasional (KIPNAS) VII, September 9 –11, 2003.

[37]Dasgupta, S., Ionnidis, A., & Agarwal, D. (2000). Infor-mation Technology Adoption in the Greek BankingIndustry. Journal of Global Information TechnologyManagement, Vol. 3 No. 3, pp. 32-51.

[38]Dasgupta, S., Agarwal, D., Ioannidis, A. &Gopalakrishnan, S. (1999). Determinants of Informa-tion Technology Adoption: An extension of existingmodels to firms in a Developing Country. Journal ofGlobal Information Management, Vol. 7 No. 3, pp. 30-40.

[39]Hofstede, G. (1980). Culture’s consequences: Interna-tional differences in workrelated values. Beverly Hills,CA: Sage Publications.

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Informatics Department – Faculty of Technology InformationInstitut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia

[email protected], [email protected],[email protected]

AbstrakThe edge detection approach based on minimal spanning tree and vector order statistic is proposed. Minimal spaningtree determined ranking from the observations and identified classes that have similarities. Vector Order Statistic view acolor image as a vector field and employ as a distance metrics. Experiment of edge detection on several images show thatthe result of minimal spanning tree is more smooth and more computational time comparing to that vector order statistic.

Keywords: edge detection, Minimal Spanning Tree, Vector Order Statistics.

1. Introduction

Saturday, August 8, 200914:45 - 15:05 Room L-211

COLOR EDGE DETECTION USING THE MINIMAL SPANNINGTREE ALGORITHM AND VECTOR ORDER STATISTIC

Edge detection is a very important low-level vision opera-tion. Despite the fact that a great number of edge detec-tion methods have been proposed in the literature so far,there is still a continuing research effort. Recently, the maininterest has been directed toward algorithms applied tocolor [1] and multispectral images [6] , which also have theability to detect specific edge patterns like corners andjunctions [2]. Edges are defined, in digital image process-ing terms, as places where a strong intensity change oc-curs. Edge detection techniques are often required in dif-ferent tasks in image processing and computer vision ap-plied to areas such as remote sensing or medicine, to pre-serve important structural properties, image segmentation,pattern recognition, etc [7]. Another method to edge de-tection is using YUV Space and Minimal Spanning Tree[8].

Scalar order statistics have played an important role in thedesign of robust signal analysis techniques.Statistic ordering can be easily adapted to unvaried data,but for multivariate data, it must go through preprocess-ing before it can be ordered. For this Vector Order Statisticmethod, R-ordering is used because based of test result; itis the best method to be used on color image processing.In this work, a new approach for ordering and clusteringmultivariate data is proposed. It is based on the minimal

spanning tree (MST) [5] and takes advantage of its uniqueability to rank multivariate data, preserve hierarchy andfacilitate clustering. The proposed method can detect allthe basic forms of edge structures and is suited for coloror multispectral images of higher dimensions.

2. Vector Order StatisticIf we employ as a distance metric the aggregate distanceof Xz to the set of vectors XI, X2,. . . , X”, then(1)By ordering every for every vector in the set, we can have(d(1) d” d(2) d” … d” d(n)), which is in line with :X(1) d” X(2) d” … d” X(n)In this work a color image is viewed as a vector field [1],represented by a discrete vector valued function f(x) : Z2ÀÛÆÜ Zm, where Z represents the set of integers. For W, n is the size (number of pixels) of W, f(xi) will be denotedas Xi. X (i) will denote the ith ordered vector in the win-dow according to the R-ordering method where the aggre-gate distance is used as a distance metric. Consequently,X(1) is the vector median [3] in the window W and X(n) isthe outlier in the highest rank of the ordered vectors Onthis behalf, the base method for edge detection is VectorRanking (VR), in which

Bilqis Amaliah, Chastine Fatichah, Diah Arianti

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(2)3. Minimal Spanning Tree

A different approach is adopted here for ranking a set ofobservations from a vector valued image.Using the MST, multivariate samples are ranked in such away that the structure of the group is also made clear.Graph theory sketches the MST structure with the follow-ing definitions [5].A graph is a structure for representingpair wise relationships among data. It consists of a set ofnodes V = {Vi }i=1:N and a set of links E = {Eij}i#j betweennodes called edges.Applied in the description of a vector-valued image, it isrepresented by a graph G(V,E). Each node Vi correspondsto a pixel, while the undirected edge Eij between two neigh-bor pixels (i, j ) on the image grid has a scalar value equal toEuclidean distance of the corresponding vectors. A tree isa connected graph with no cycles. A spanning tree of a(connected) weighted graph G(V,E) is a connected subgraph of G(V,E) such that (i) it contains every node ofG(V,E),2 and (ii) it does not contain any cycle. The MST is aspanning tree containing exactly (N “ 1) edges, for whichthe sum of edge weights is minimum.In what follows, the method is restricted for color RGBimages (i.e. p = 3) and in the case of a 3 ×3 rectangular sliding window W. However, the method ismore general as it can be applied to higher dimensions andby using a window of an other size and/or shape. Given aset of N = 9 vectors corresponding to the pixels inside W,the Euclidean MST (represented by T) is constructed inR3.Considering the edge types we would like to detect, threepossible color distributions [3] can be usually found in-side W. If no edge is present and the central pixel is lo-cated at a uniform color region of the image, the distribu-tion is unimodal denoting a “plain” pixel type. If there is an“edge” or “corner” point, a bimodal distribution is ex-pected. Finally, in the case of a “junction”, pixels are ex-pected to form three clusters. Thus, edges and corners arestraight and angular boundaries of two regions, whereasjunctions are boundaries of more than two regions.

4. System Design

In Minimal Spanning Tree method implementation, thereare 3 main process, which are the calculation of the dis-tances between neighboring pixels, finding the MinimalSpanning Tree route, and the deciding the output type(plain, edge, corner, or junction). Those three processesare done in a sliding window that the size is already de-fined, which is 3x3.For that reason, the original input image matrix must be

added with one pixel width of pixel on each side, so thatthe output of the pixels at the edge of the original imagecan be calculated.The distances of neighboring pixel are calculated usingEuclidean Distance. Then the Minimal Spanning Tree routecan be determined using Kruskal Algorithm. T1 variablewas defined as a threshold parameter and T as total lengthto determine the pixel type. Next is the pixel type determi-nation algorithm.The process of the proposed method is summarized withinthe following steps:• Construct the MST.• Sort the derived MST-edges E1,E2, . . . , E8 in ascendingorder.• Denote as “T” the total length of the MST.• Define threshold parameter “T1” so that 0_T1_1.• If (E7/E8_T1) then unimodality exists (one distribution)’! plain pattern.’! R1 = mean(T ) is the detector’s output.• Else if (E6/E7_T1) then bimodality exist (two distribu-tions)’! edge or corner pattern.’! Cut the maximum edge E8 (two subtrees are generated,thus two separate clusters).’! Find the mean value of the two clusters C1 and C2.’! R2 = is the detector’s output.• Else, multimodality exist (three distributions) ’! junctionpattern.’! Cut the two bigger edges, E7 and E8 (three subtrees aregenerated, thus three separate clusters).’! Find the mean value of each of the three clusters Ci, i = 1: 3.’! Compute the distance between the three cluster centers.Rij = , i, j = 1 :3 for i = j .’! R3 = mean(Rij) is the detector’s output.

5. Experiment Result

The images that used are lena.bmp, peppers.bmp,house.bmp, dan clown.bmp.5.1 MST Method Test Using T1 VariationThe goal of this test is to prove whether changes in T1value affect the edge detection process.Based on the result on Figure 1, T1 value that gave bestresult is 1.0 on all samples images.

5.2 MST and VOS Comparison Test5.2.1 Edge Quality TestFrom the test result in Figure 2, it is seen that the result ofedge detection using Minimal Spanning Tree Method gaveedges that are more solid and not separated. Meanwhileusing Vector Order Statistic gave edges that are not solid

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and sometimes not connected between each other.T1 = 0.7 T1 = 0.8 T1 = 0.9 T1 = 1.03T1 = 0.7 T1 = 0.8 T1 = 0.9 T1 = 1.0T1 = 0.7 T1 = 0.8 T1 = 0.9 T1 = 1.0T1 = 0.7 T1 = 0.8 T1 = 0.9 T1 = 1.0Figure 1. Result detection using MST with threshold varia-tionCitra Masukan Minimal Spanning Tree Vector Order Sta-tistic4Figure 2. Result comparison between MST and VOS baseon edge quality

5.2.2 Algorithm’s Execution Time TestCitraMasukanMinimalSpanning TreeVector OrderStatisticElapsed time is398.900481seconds =6.6483 minutes.(MST = 100%)Elapsed time is14.334815seconds =0.2389 minutes.(VOS = 3,59%)Elapsed time is416.205385seconds =6.9368 minutes.(MST = 100%)Elapsed time is14.242192seconds =0.2374 minutes.(VOS= 3.422%)Elapsed time is398.900481seconds =6.6483 minutes.(MST= 100%)Elapsed time is14.334815seconds =0.2389 minutes.(VOS= 3.549%)Elapsed timeis 26.049380seconds =

5.434minutes.(MST = 100%)Elapsed time is14.242192seconds =0.2374 minutes.(VOS= 4.388%)Figure 3. Result comparison and procentase between MSTand VOS base on algorithm’s time execution.From the result in Figure 3, it is clearly seen that MinimalSpanning Tree method takes longer time to finish thanVector Order Statistic method.

6. Result Evaluation

6.1 Best Threshold for MST MethodT1 value that gives best result on all sample images is1.0.5The correlation between T1 and the detector output are asfollow:If (E7/E8 >T1) then Result = plainelse if (E6/E7 > T1) then Result = edge orcornerelse Result = junctionIn this method, if E7 equal to E8 then it can be concludedthat the pixel in observation is plain.The pixel is considered edge or corner if E6 equal to E7while E7 not equal to E8.Last, if E6, E7 and E8 are all not equal, then the pixel can beconsidered as junction.Data on Figure 3 was tested using T1 = 0.7 or T1 = 0.8,which gave plain type result, meanwhile if tested using T1= 0.9 or T1 = 1.0, it was detected by the detector as junc-tion.From the experiments it is known that the increment of T1value makes the sensitivity of the detector increases.Changes on T1 value can be used to get different detectorsensitivity.

6.2 Edge Quality ComparisonVector Order Statistic is a method that orders the sum ofdistances between pixels in a sliding window.A pixel has distance to all other pixels in the sliding win-dow, including to itself. That distance is calculated usingEuclidean distance equation.In a uniform area, each vector will relatively close to eachother and the distance value is smaller. Output value fromthis method is the average of the distances between pixelsneighboring the pixel in question.Minimal spanning tree is a method that can rank data in aset into clear groups. Because of the nature of this method

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in which it only considers the neighboring pixel in thesliding window, the correlation between pixels is preserved.The point is, that the edge detection process put empha-sis at the relationships.Minimal Spanning Tree method also group pixels into clus-ters based on color similarities between neighboring pix-els. By using the distances between each clusters as de-tector output, the edge resulted is finer.

6.3 Algorithm’s Execution Time ComparisonBased on the experiments, execution time needed for thisMST method is longer than VOS method with averageratio of MST : VOS = 27 : 1. This is not a small ratio.For the VOS, the method doesn’t do much iteration. Allpixel type is processed using the same way, which is byusing the defined equation.Meanwhile for MST method, it is known that this methodis doing a lot of iteration, for defining the cluster for ex-ample. If the input image has a lot of plain pixel type that isdetected by the detector, then this method will finish inrelatively less time. And if the input image has a lot ofedge/corner pixel type, then this method will finish in longertime. It is caused by the creation of 2 clusters.

7. Conclusion

From the experiments done, some conclusions found:1. Minimal Spanning Tree method gave a more solid line

than edge result from Vector Order Statistic method.2. With the use of threshold parameter, detector sensitiv-

ity of Minimal Spanning Tree method can be definedaccording to the preferred result. Threshold value forbest edge detection on Minimal Spanning Tree methodis 1.

3. Minimal spanning Tree needs longer execution timethan Vector Order Statistic with average ratio for sampleimages between MST and VOS is 100% : 3.733%.

4. Edge detection result from Minimal Spanning Treemethod for images that have more detail will give sharperedge than Vector Order Statistic.

8. Suggestions

It is suggested to optimize the edge detection algorithmusing minimal spanning tree, to shorten the execution time.

9. References

[1] P.W. Trahanias, A.N. Venetsanopoulos, Color edgedetection using vector order statistics, IEEE Trans. ImageProcess. 2 (2) (1993) 259–264.[2] M.A. Ruzon, C. Tomasi, Edge, junction, and corner de-tection using color distributions, IEEE Pattern Anal. Mach.Intell. 23 (11) (2001) 1281–1295.[3] J. Astola, P. Haavisto, Y. Neuvo, Vector median filter,Proc. IEEE 78 (1990) 678–689.[4] C.T. Zahn, Graph-theoretical methods for detecting anddescribing Gestalt clusters, IEEE Trans. Comput. C 20 (1)(1971).[5] Theoharatos .Ch, Economou.G, Fotopoulos. S, Coloredge detection using the minimal spanning tree, PatternRecognition 38 (2005) 603 – 606.[6] P.J. Toivanen, J. Ansamaki, J.P.S. Parkkinen, J.Mielikainen, Edge detection in multispectral images usingthe selforganizing map, Pattern Recognition Lett. 24 (16)(2003) 2987–2993.[7] T. Hermosilla, L. Bermejo, A. Balaguer, Nonlinear fourth-order image interpolation for subpixel edge detection andlocalization, Image and Vision Computing 26 (2008) 1240–1248[8] Runsheng Ji, Bin Kong, Fei Zheng. Color Edge Detec-tion Based on YUV Space and Minimal Spanning Tree,Proceedings of the 2006 IEEE International Conference onInformation Acquisition August 20 - 23, 2006, Weihai,Shandong, China.

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Information System Department – Faculty of Computer StudyUniversitas Bina Nusantara

Jl. Kebon Jeruk Raya No. 27, Jakarta Barat 11530 Indonesiaemail :[email protected]

ABSTRACTThis report contains about one of the applications that used by PT. Indonusa Telemedia. The function of this applicationto facilitate the recruitment and selection process of the company employee’s candidate. The process becomes moreefficient because the application can organize the employee candidate data, interview status (proceed, hire, keep, andreject), and his comments based on the interview. The benefit for the company that uses this application is that they canincrease their level of efficiency, such as in time and man labor. The level of efficiency can be increase because thisapplication can sort the employee’s candidate data as the request of the department that request addition of employeeand centralizing information in one application database.

Key words : Application, Employee Recruitment, Selection

Saturday, August 8, 200915:10 - 15:30 Room L-210

DESIGN COMPUTER-BASED APPLICATION FOR RECRUITMENT ANDSELECTION EMPLOYEE AT PT. Indonusa Telemedia

Tri Pujadi

1. INTRODUCTION

PT. Indonusa Telemedia is with Brand NameTELKOMVision who gets address at Tebet in southJakarta. Executed research program deep three-monthduration, from date 1st January 2009 and end on the 08Aprils 2009. The scope of observational activity to bedone at Network’s &IT division whereas watch on HRD’Sdivision, Business Production & Customer Quality’sdivision communicates in makings carries on business toprocess, design of application. There are severally jobdescription of this activities, for example is (1) Design ofApplication Penerimaan dan Seleksi Calon Karyawan ;(2) Design of Application Koperasi ; (3) Captured thiscandidate fires an employee Indonusa Telemedia; (4)Design of Business Process;Presented result write-up deep observational one containsabout application scheme activity Penerimaan dan SeleksiCalon Karyawan utilizing Visual Basic 6.0, Ms Accessdatabase and microsoft excel’s to Report of application.PT. Indonusa Telemedia stand on the May 1997 andoperating on year 1999, with many of stockholder there

are PT Telkom, PT Telkomindo Primabhakti (Megacell),PT RCTI and PT Datakom Asia. On year 2003,TELKOMVision has Head End at six metropolises whichis Field, Jakarta, Bandung, Semarang, Surabaya andJimbaran Bali and some mini Head End at all Indonesia.Network support Hybrid Fiber Optic Coaxial andcoverage satellite at Indonesian exhaustive one playsalong with Telkom as Holding Company make its as theone only of Operator Pay TV one that has ability to servicecustomer at Indonesian exhaustive good utilize Satelliteor Cable.

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2. Human Resource Management

Terminologicals of human resource management byDessler (2008) is activities various management to increaselabour effectiveness in order to reach organization aim.Human resource management process (SDM) constitutingactivity in saturated logistic requirement fires an employeeto reach employee performance. Meanwhile (Parwiyanto,2009), SDM’S planning constitutes to analysis processand identification most actually requirement will man soorganization resource that can reach its aim. There bemany procedures SDM’S planning, which is:· Establishing qualities clear all and needed SDM

amount.· Gathering data and information about SDM.· Agglomerating data and information and analysis this.· Establish severally alternative.· Choosing the best one of taught alternative becomes

plan.· Informing plan to employees for realized PSDM’S

method (Human resource planning) known in twomethods, which is method scientific or non-scientificmethod. That non-scientific method are planning SDMjust is gone upon for experience, imagination, andestimatesonly. SDM’S plan this kind of its risk a greatdegree, e.g. quality and labour amount in conflict with

firm requirement. It can ensue on arises itmismanagement and adverse dissipation corporate.The scientific method of PSDM is done by virtue ofresult of data analysis, information, and forecasting(forecasting) of its planner. SDM’S plan this kind ofrelative’s risk little because all something it was takeninto account beforehand. If SDM’S planning is putacross therefore will be gotten benefits of as follows:

· Top management to have the better view to SDM’Sdimension or to its business decision.

· Expenses SDM wills be smaller because managementcan estimate things that don’t at wants that can ensueto swell needed cost it.

· Most actually more a lot of time to place clerk thatpotentially because requirement can be anticipated andis known before total labour that actually been needed.

· Mark sense the better chance to involve woman andthe few faction at strategical deep proximately.

Clerk acceptanceFunction of recruitment clerk is look for and pulls clerkcandidate to want apply for works according to jobdescription and, job specification. For the purpose thatfirm can look for clerk candidate of internal source andexternal’s source. Each source has gain and lack.Advantages of clerk acceptance by internal source.1. Stimulating preparation for transfer and promotion.2. Increasing job spirit.3. More information a lot of about candidate can be gotten

Figure 1. Organization Chart PT Indonusa (2009)

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from work note at corporate.4. Less expensive and was ready conforms.

But the disadvantages is:1. Drawing the line clerks prospective source.2. Reducing new view source chance.3. Pushing smug taste especially if stipulates responsible

position rise be seniority.

Advantages of clerk acceptance by external’s source iscan pull advertising pass, its source is Depnaker, educationinstitute, consultant office, alone coming applicant, andextent society as labor market. But on the other hands,disadvantages is the process adapts clerk to slowertending firm instead of clerk which stem from within firm.

Clerk selectionChoosing candidate to be able to prospective onecorresponds to to talk shop that available by:1. Checking application document and stipubting document that shall be attached in application letter.2. Interview advancing to checks truth written document.3. Diagnostic test, skill, health, can own do by corporate / can do extern party.4. Background research of other source at work previous.

Training and DevelopmentTraining terminology is utilized to increase technicalmembership. Development is utilized to increaseconceptual membership and human relationship. Therebe three requirement prescriptive processes training,which is:1. Appraisal achievement compared with by default, if haven’t reached matter default is required training.2. Analisis is requirement talks shop, which is employee which haven’t qualified given by training.3. Survey of personel, asking faced problem and training what does they require.

Seven training’s forms, which is:1. On the job training2. Job rotation3. Internship, appointment brazes and field practice.4. Appretionship5. Off the job training6. Vestibule training, simulation talks shop that don’t trouble others. Example: seminar, college.

7. Behavioural training, training pass business game and role playing.

Definition of Design SystemAccording to Whitten (2005) on binds books SystemAnalysis And Design For Enterprise: Design of system isprocess one to get focus on detail of solution that basesinformation system. That thing can also be said as designof physical. On system analysis moring to reassurebusiness cares, meanwhile on system scheme gets focuson technical problem and implementation which pertinentwith system. To the effect main of design of system issubject to be meet the need system user and to give clearcapture and design that clear to programmer.Severally phase in design of system is:1. Design of control, its aim that implementing system afters can prevent fault’s happening, damage, system failing or threat even system security.2. Design of output, on this phase reporting of resultant one shall correspond to needful requirement by application user.3. Design of input, on this phase GUI’S scheme( Graphic User Interface) made for the purpose more of eficient input data and data accuracy.4. Design of database are an information system that integrate bulk of interrelates data one by another.5. Design of computer configurations to implement the systems

3. Research result

Telkom has Vision To become a leading InfoCom playerin the region, meanwhile its Mission is give to service “One Stop InfoCom Services with Excellent Quality andCompetitive Price and To Be the Role Model as the BestManaged Indonesian Corporation.” Unit Carries OnBusiness Telkom consisting of division, Centre,Foundation and Subsidiary. To subsidiary Telkom havestock ownership is more than 50%, for example one of it,is on PT Indonusa Telemedia (Indonusa)

The Product and ServicesIn the early year month of July 2000, PT. IndonusaTelemedia has begun to do INTERNET service attempt.Now that service finitely can be enjoyed by achievablecustomer by Hybrid Fiber Optic Coaxial (HFC networks)at Jakarta, Surabaya and Bandung.Many several product and servives from PT IndonusaTelemedia is :

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a) Pay TV Cable TELKOMVision :Basic service was included channel HBO’S main,CINEMAX and STAR MOVIES. ; Utilizing FO’sinfrastructure from Telkom. ; PRIMA pictured quality.; Tallreliability & interactive network (two aims) for Internetand further can service VOD, Video Streaming; Withoutdecoder / Converter ( TV Cable) ; If in one house existsmore than one TV, therefore each TV can enjoy channeloption each independent ala without shall add decoderand also converter for each one TV.

b) TELKOMVision’s internet :Flat fee without pulse count.; Speed 64 s / d. 512 Kbps.;Be of service service corporate & individual. ; Buildingwith Pay TV – Cable.

c) Pay TV Satellite TELKOMVision :Utilizing TELKOM’S satellite –1, extended C band.; Primapictured quality and digital voice. ; If customer candidatehave had parabola at place that will be assembled, thereforenot necessarily substitutes parabola.; Coverage :NATIONAL

d) SMATV TELKOMVision :Satellite Master AntenaTV (SMATV) service.; Locationthat was reached network HFC. ; Customer Hotel,Apartment or Estate Settlement.; Coverage : NATIONAL

Total employee on PT IndonusaTable 1.1 Number of employees per job unit

Business process on recruitment and selection1. Related User or directorate ask for clerk affix fill

recruitment requisition form.2. After form accepted recruitment by HRD then staff

HRD checks budget and organization chart oncorporate. Then staff HRD publishes vacancy throughmedia or notice to corporate clerk.

3. Candidate fires an employee to send application letterand biographically to HRD.

4. Staff HRD sort this candidate corresponds to criterionthat being needed by according to requisition user.

5. Prospective denominating fires an employee to be doneby HRD and interview done by user adjoined by staffHRD.

6. If afters interview user looking on that employee candidate criterion pock, therefore candidate data fires

an employee to be kept on for phase interview hereafter.7. User have final HRD’S party do wages negotiation

Figure 2. Business process on recruitment and selection

Table 1.1 Number of employees per job unit

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with prospective employee.8. If employee candidate accepts to wages negotiation,

HRD will publish offering letter for candidate to firean employee refuses wages negotiation, HRD can lookfor another candidate.

After candidate accepts an offering letter therefore thatnew employee shall fill candidate form and can begininternship term at corporate up to three-month.

Design of application :a. Design of ControlOn this application there be two types login. Every userhave level one that different. Type following – typelevelthe:1. ADMINISTRATOR : Have rights to utilize all thisapplication function.2. HRD : Just utilizes function input prospective datafires an employee, interview, comment and report.

b. Design of OutputOutput or reporting result of that application as statisticalof denominating amount interview candidate fires anemployee in any directorate and sidelight hit stateinterview of employee candidate.

Figure 3. Report Statistik Interview

c. Design of InputOn Accepting application and Employees

Prospective selection be gotten four menus and eightmenu subs, which is:1. File2. Application3. Interview4. Repor

Figure 4. Account Setting

Input Applicant data, function of menu that is subject tobe input prospective data fires an employee. Prospectivedata source employee can thru get Enamel( soft copy) andapplication letter gets to form hard copy.

Figure 5. Input Applicant

Input Data Interview, in function for memasukan appraisalof each interviewer for each called employee candidateand for each step interview. Menu it also been utilized toprocess candidate more employee already at interview, isthat employee prospective will be drawned out to nextphase or not.

Figure 6. Input interview

Report function report of that application just getsstatistic form of report base to see dammed hell firstemployee increase per division. That thing is done thatHRD Dapa monitors to foot up step-up or requisitiondecrease fires an employee new on each division.

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Figure 7. Report function

c. Design of database

4. Summary

Base job process of acceptance Application and Candidateselection an employee :· Can do to validate schedule interview and interviewer

easily.· Prospective bespoke statistical employee can presto

get since statistical at o self acting and is featured atexcel’s Microsoft, so more data processing can be doneby easier.

· This application can direct be printed or is kept intoMicrosoft format form Excel 2003( .xls).

REFERENCES

Kroenke, David M.(2005) Database Processing Dasar-dasar, Desain, dan Implementasi. 2 vols. Trans. Nugraha,

Figure 8. Design of Database

Dian. Jakarta: Erlangga, Trans. Of Database ProcessingFundamental, Design & Implementation, 2004.

Firdaus. (2005) Pemrograman Database dengan Visualbasic 6.0 untuk Orang Awam. Palembang: Maxikom.

Parwiyanto, Herwan. Perencanaan SDM. 3 Maret 2009<http://herwanparwiyanto.staff.uns.ac.id/page/2/>.

Dessler, Garry, (2008) Human Resource Management.Singapore: Pearson Edication Singapore.

Whitten,(2005) System Analysis And Design ForEnterprise. Prentice Hall.

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Paper

Untung RahardjaFaculty of Information System

Raharja UniversityTangerang, Indonesia

[email protected]

Retantyo WardoyoFaculty of Mathematics and Natural Science

Gadjah Mada UniversityYogyakarta, Indonesia

[email protected]

Shakinah BadarFaculty of Information System

Raharja UniversityTangerang, Indonesia

[email protected]

AbstractAlong with rapid development of network and communication technology, the proliferation of online information re-sources increases the importance of efficient and effective distributed searching in distributed database environmentsystem. Information within a distributed database system allows users to communicate with each other in the sameenvironment. However, with the escalating number of users of information technology in the same network, the systemoften responds slowly at times. In addition, because of large number of scattered database in a distributed databasesystem, the query result degrades significantly in the occurrence of large-scale demand at each time data needs. Thispaper presents a solution to display data instantly by using Data Mart Query. In other words, Data Mart Query (DMQ)method works to simplify complex query manipulating table in the database and eventually creates a presentation tablefor final output. This paper identifies problems in a distributed database system especially display problem such asgenerating user’s view. This paper extends to define DMQ, explain the architecture in detail, advantages and weaknessesof DMQ, the algorithm and benefits of this method. For implementation, the program listings displayed written ASP scriptand view the example using DMQ. DMQ methods is proven to give significant contribution in Distributed DatabaseSystem as a solution that is needed by network users to display data instantly that is previously very slow and inefficient.

Index Terms—Data Mart Query, Distributed Database

Saturday, August 8, 200913:55 - 14:15 Room L-212

Application of Data Mart Query (DMQ)in Distributed Database System

I. IntroductionThe development of technology that continues to increaserapidly, affecting the rate of information on human needs,

especially in a organization or company. Information con-tinues to flow and the longer the amount increasing as thenumber of requests, the amount of data and more. In addi-

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tion, the use of databases in a company and the moreorganization especially with the network system. The da-tabase can be distributed from one computer to another.The user can increased the amount of flow over the size ofthe organization or company.Organizations and companies need information systemsto collect, process and store data and an information chan-nel. Development of information systems from time to timehave produced a lot of information that the more complex.The complexity of information is caused by the many re-quests, the amount of data and the level of Iterations SQLcommand in a program .

Utilization of information technology by organizations orcompanies are the major aims to facilitate the implementa-tion of business processes and improve competitive abil-ity. Through information technology, the company ex-pected business processes can be implemented more eas-ily, quickly, efficiently and effectively. The use of networktechnology in an organization or company to become aregular thing. A system within the network now many or-ganizations and companies that have implemented a data-base system for distributed database.Distributed Database is a database that is under the con-trol where the central DBMS storage is not centralized toa CPU, but may be stored on multiple computers in thesame physical location or distributed through a networkof computers that are connected each other.

Figure 1. Distributed Database Architecture

Picture above, the architecture is a description ofthe database distributed. Where in the system distributeddatabase allows several terminals connected in a systemdatabase. And each terminal can access or obtain datafrom the database that have both computer centers and alocal computer or a database with other databases. Data-base distributed also have advantages such as organiza-tional structure can reflect, local autonomy. Error in onefragment does not affect the overall database. There is abalancing of the database server and the system can bemodified without affecting other modules.Therefore, the data storage table in SQL server in a dis-tributed database, is a practical step that many people doat this time. For institutions serving a specific process

data should be fast, for example, on google.com wherecontinuous data is desired by consumer to be quickly lo-cated in the display, not the little data that must be re-moved. Basically, it requires a table of data from the pro-cess according to the number of data that will be in theshow. Use of the conventional way is essentially a practi-cal, because they do not need when editing data in thedatabase increases, but if the speed display will be longwhen a lot of data that is displayed.

II. PROBLEMSDistributed database that has many advantages especiallyfor the structure of the organization at this time. However,among the benefits of the distributed database also allowsa system more complex, because the number of databaseswhich are spread and the amount of data and many con-tinue to increase in an organization or company. If a data-base has a number of data stored with the many queriesand tables, a request the search result data source or datato be slow addition, the number of users that can access aweb display or display a Web information system is also aslow .. Here was the view of conventional sources of datathat have multilevel query:

Figure 2. Conventional Data Source

From the picture above, we can see that to pro-duce a display on the web display, the conventional sourcesof data need to be stratified queries. Source of data is donefrom one table and another table and to query the query toone another. Imagine if you have hundreds or thousandsof tables and queries in a database, and database distrib-uted so happens that the relationship between the data-base with each other. How long does it take only one viewto the web?From the above description, several problems can be for-mulated as the following:1. Whether the query view because research has been

graded?2. What is the impact of a slow process due to stratified

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queries?3. What methods can be used to speed up the process

on a display distributed database system?4. What benefits and disadvantages with the new method

is proposed?

III. LITERATURE REVIEW

Many of the previous research done on the dis-tributed database. In developing distributed database thisstudy should be performed as one of the libraries of theapplication of the method of research to be conducted.Among them is to identify gaps (Identify gaps), to avoidre-creating (reinventing the wheel), identify the methodsthat have been made, forward the previous research, andto know other people who specialize, and the same area inthis research. Some Literature review are as follows:

1. Research was conducted by Jun Lin Lin and MargaretH. Dunham’s Southern Methodist University and MarioA. Nascimento, entitled “A Survey of Distributed Database Check pointing.” This study discusses thecheck pointing in the database distributed and approaches used. This study begins from the many surveys conducted in connection with the database recovery process, and many techniques proposed to address them. With the distributed database check pointing, the process can reduce the recovery time of a failure in the database distributed. Check pointing can bedescribed as an activity to write information to a stablestorage during normal operation in order to reduce theamount of work at the restart. Disprove that this research and a little bit limitation of resources is a problem in the approach distributed database, and disprovethat check pointing can be used only for the distribution of many multi database system. Although this research has been done, but quite complex in its implementation. With this research we can develop a database distributed with check pointing to speed up theprocess of recovery database[1].

2. Research was conducted by David J. Dewitt from theUniversity of Wisconsin and Jim Gray in 1992, entitled“Parallel Database Systems: The Future of High Performance Database Processing”. Research was conducted with the concept of database distributed whichis a database stored on several computers that distributed one another. On this research, described paralleldatabase system began replacing Mainframe computerfor data and transaction processing tasks. Parallel database machine architectures have evolved from theuse of software for exotic hardware that parallel. Likemost applications, users want the system’s hardwaredatabase that cheaper, fast. This concern about theprocessor, memory and disk. As a result, the concept

of exotic hardware that the database is not appropriatefor the technology at this time. On the other hand, theavailability of microprocessors faster, cheaper andsmaller to be cheaper but the standard package soquickly become the ideal platform for parallel databasesystems. Stonebraker proposes a simple design to thedesign spectrum that is shared memory, shared diskand shared nothing. And the language used in the SQLdatabase is in accordance with ANSI and ISO standards. With this research, we can develop a databasesystem that can be used in different scope [2].

Figure 3. Shared-Nothing Design, Shared-Memory and

Shared-Disk

3. Research was conducted by Carolyn Mitchell of Norfolk State University, entitled “Components of a Distributed Database” 2004. This study discusses the components within the database. One of the main components is in DDBMS Database Manager. “A DatabaseManager is the software responsible for processingthe data segments that were distributed. The main components are the Query User Interface, which is a clientprogram that acts as an interface to the TransactionManager distributed ..” Distributed a Transaction Manager is a program that translates requests from usersand convert them to query the database manager,which is usually distributed. A system database thatdistributed of both the manager and the TransactionManager Database Manager Distributed[3].

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Figure 4. Distributed Database Architecture and compo-nents

4. Research conducted by Hamidah Ibrahim, “DerivingGlobal Integrity and the Local Rules for DistributedDatabase. Faculty of Computer Science and Information Technology University Putra Malaysia, 43400 UPMSerdang. He said that the most important goal of thedatabase system is a guarantee data consistency, whichmeans that the data contained in the database must beproperly and accurately. In the implementation to ensure consistency of data is very difficult to change,especially for distributed database. In this paper, describes an algorithm based on the rule enforcementmechanism for the distributed database that aims tominimize the amount of data must be transferred oraccessed across the network to maintain the consistency of the database at one site, the site at which theupdate needs to be done. This technique is called theintegrity of the test generation, which comes from local and global integrity, and rules that have been effective to reduce the cost of a check constraint in the datathat has been distributed in the environment. In hisresearch has produced a large centralized system witha high level of reliability for data integrity[4].

5. Research conducted by Steven P. Coy. “Security Implication of the Choice of Distributed Database Management System Model: Relational Object Oriented Vs.University of Maryland that data security must be addressed when developing a database of them andchoose between Relational and object oriented model.Many of the factors must be considered, especially interms of effectiveness and efficiency, as well as securities and whether the integrity of this food resource istoo large not only the security features. Both theseoptions will affect the strength and weaknesses ofthese databases. Centralized database for both thismodel can be as well. But for the distributed database,

Relational model is superior in securities. This is because more object oriented database model is still lessmaturities. So that in the heterogeneous environment,the process integrities still cause many problems.OODBMS still only technology still needs further development, but in the homogenous environment,OODBMS can be a good choice[5].

6. Research conducted by Stephane Gançarski, ClaudiaLeón, Naacke Hubert, Martha Rukoz and Pablo Santini,entitled “Integrity Constraint Checking in Distributednested transactions over a Database Cluster” is a solution to check the integrity and global constraints inmulti-database related systems. This study also presents the experimental results obtained on a PC clustersolutions with Oracle9i DBMS. Goal is to experiment tomeasure the time spent in the check constraints in theglobal system that distributed. Result shows that theoverhead is reduced to 50% compared with the integrity of the examination center. Studies show that thesystem for possible violation of referential integrityconstraints and global conjunctive. However, with thedistributed nested transactions, with the execution andparallelism, the integrity can be guaranteed[6].

7. Research was conducted by Allison L. James PowellC.dkk, France Department of Computer Science University of Virginia, entitled, entitled The Impact of Database Selection on Distributed Searching. This studyexplains that distributed searching consists of 3 parts,namely database selection, query processing, and results merging. Self-made database that some databaseselection (not all) and the performance will increasequite significantly. When the selection is done with agood database, the search is distributed akan performbetter than the centralized search. Searching the database is also added to the selection process and theranking so that the potential to increase the effectiveness of search data[7].

8. Research was conducted by Yin-Fu Huang and HERJYH CHEN (2001) from the National University of Science and Technology Yunlin Taiwan, entitled fragmentAllocation in Distributed Database Design. On this research about the Wild Area Network (WAN), fragmentallocation is a major issue in the distribution databasedesign as a concern on the overall performance of distributed database system. System proposed here issimple and comprehensive model that reflects the activity in a distributed database. Based on the modeland transaction information, the form of two algorithmsdeveloped for the optimal allocation of the total cost ofsuch communication be minimized as much as possible. The results show that the allocation of fragmentation is found by using the appropriate algorithm will

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be more optimal. Some research is also done to ensurethat the cost of formula can truly reflect the cost ofreal world communication[8].

9. Research conducted by this Nadezhda Filipova andFilcho Filipov (2008) from the University of Economics. Varna, Bul. Kniaz BorisI entitled Development of adatabase for distributed information measurement andcontrol system. This study describes the developmentof a database of measurement information and distributed control systems that apply the methods of opticalspectroscopy for plasma physics research and atomiccollisions and provides access for information and resources on the network hardware Intranet / Internet,based on a database management system on the Oracle9idatabase . The client software is realized in the JavaLanguage. This software is developed using the architecture model, which separates the application datafrom graphical presentation components and input processing logic. Following graphical presentations havebeen conducted, the measurement of radiation fromthe plasma beam Spectra and objects, excitation functions of non-elastic collisions of heavy particles andanalysis of data obtained in the previous experiment.Following graphical client that has a function of interaction with the browsing database information about aparticular type of experiment, the search data with thevarious criteria, and enter information about the previous experiment[9].

10. Research conducted by Lubomir Stanchev from theUniversity of Waterloo in 2001, entitled “Semantic DataControl in Distributed Database Environment”. Thisresearch states that there are three main goals in thesemantic data control, namely: view management, datasecurity and semantic integrity control. In a relationship, these functions can achieve uniformity with enforcing the rules control the data manipulation. Thesolution is to centralization or distributed. Two mainissues to make efficient control is the definition of dataand storage rules (site selection) and the enforcementof design algorithms that minimize the cost of communication. The problem is difficult, because the increasein function (and general) tend to increase the communication site. Solutions to semantic data control distributed is the existence of centralized solutions. Theproblem is simple if you control the rules fully replicated at all sites and site autonomy difficult if patented.In addition, the specific optimization can be done tominimize the cost of control data, but with additionaloverhead such as management of data snapshots.Thus, the specification control distributed data mustbe included in the database design so that the cost toupdate the control programs are also considered[10].

Figure 5. Data Visualization with materialized views andAuxiliary

Literature review of the ten who have, have a lot of re-search on check pointing, parallel database system, dis-cussion of the component database system, is also aboutsecurity. Besides, there is also discussion about the nestedtransaction, distributed searching, view management andfragment allocation. However, it can be that there is noresearch that specifically discuss the issue or view a slowprocess due to stratified queries.

IV. TROUBLESHOOTING

To overcome the above issues, the process required a fastand efficient access to all data in a more organized and notin the database, especially for a system database that dis-tributed. Currently, programmers prefer to use Ms Accessand query functions for the entire script command. Conse-quently the process of large-scale query occurs every needdata. The use of SQL server is not a new thing in this case,therefore proposed for the establishment of a system toprocess more at the time of loading and presentation ofdata have a linear speed faster than the conventional way.DMQ (Mart Data Query) is a method of applying the anal-ogy “Waste Space for Speed.” DMQ is also one of themethods of forming the separation between the “Engine”and “Display”. In other words DMQ method can displaythe source code directly on the display and process thequery is done on the engine. DMQ generally produce adisplay that data far more quickly than by using commonmethods, as DMQ does not do it again in the process ofdisplaying data. DMQ and finally a solution that can helpthe needs of users on the display data, previously veryslow and does not efficient.Query on the Data Mart data sources come from the table.

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So in this process DMQ, allocate all of the selected datainto a table. So user does not need to consider the pur-pose of making the structure of the table, which should beconsidered only where the data is located. DMQ is used toavoid the use of complex queries. DMQ will compromisingthe amount of data storage capacity (space hard disc) toincrease speed (increase speed). DMQ need trigger up-date data to generate the data current.The following is a description of the request data from theuser. Where a user make a request will display, and dataquery module to search on db1, db2 to dbn. By using thedata mart or queries DMQ query data from the moduledirectly queries want in the form of a graphical displaymodule that can be viewed by the user.

Figure 6. Data Visualization with DMQ

With Query Data Mart (DMQ) process data faster, notbecause the conventional data source such as the need tofind from the table. Data Mart Queries (DMQ) can cut thetime because the process of search data to only one tablethat have been merged.

Figure 7. Comparison of conventional data sourcesand the source data with the Data Mart Query

If compared will looks like the picture above. DMQ whichcan make viewing the web more quickly done because theprocess does not require a complex search. This can alsobe evidenced in the graph 1.

Description: = Not Use the DMQ

= Use the DMQ

Figure 8. Comparison of Time and Number of data

In the chart above, you can see how the compari-son time and the amount of data to display a web in whichthe amount of data for each graph the value is the same.The graph above explains that if a view does not use theDMQ, then graphed will rise above, or the greater amountof data then the process will be long. However, if using theDMQ for view the data regardless of the time needed toprocess view relatively constant.

A. Linear Regression Besides the proven graphics, can also be evidencedwith the exponentially Linear regression equation as fol-lows:

Y’= a + bX

Wherea = Y shortcuts, (the value of Y ‘when X = 0)b = slope of regression line (increase or decrease in Y ‘foreach one-unt change in X) or regression coefficients, whichmeasure the amount of the influence of X on Y when Xincreased unitX = value of variable-freeY ‘= a value that is measured / calculated on a variable notfree

Values a and b in the regression equation can becalculated with formula below:

a = é - X ………[11]

· Linear regression calculation for the view with-out using the DMQHere is the data obtained when not using the Data MartQuery:

Summary of data Time x y xy y2

x2

X Y (X – X) (Y- é) 5000 43 -22500 -80.25 1811250 6480.25 50625000010000 79 -17500 -44.5 778750 1980.25

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30625000015000 78 -12500 -45.5 568750 2070.25 15625000020000 103 -7500 -20.5 153750 420.25 5625000025000 118 -2500 -5.5 13750 30.25 625000030000 124 2500 0.5 1250 0.25 625000035000 159 7500 35.5 266250 1260.25 5625000040000 141 12500 17.5 218750 306.25 15625000045000 189 17500 65.5 1146250 4290.25 30625000050000 201 22500 77.5 1743750 6006.25 506250000275000 1235 6702500 22844.5 2062500000

Table 1. Data and calculations for the view without theDMQ

X = 27500

é = 123.5

From the above calculation, the value of a and b are calcu-lated as follows:

= 0.0032

a = é - X = 123.5 – 0.0035 (27500) = 35.5

So, the regression equation that shows the relation-ship between the number of both variable data and a timeto view the display is:

Y = 35.5 + 0.0032X

So each time the amount of data increases the timethe process will be 0.0032 time.

· Linear regression calculation with a view to us-ing the DMQHere is the data obtained by using the Data Mart Query:Table 2. Data for the calculation and view the DMQSummary of Data Time x y xy y2

x2

X Y (X – X) (Y- é) 5000 11 -22500 -7 157500 49506250000

10000 16 -17500 -2 35000 430625000015000 18 -12500 0 0 015625000020000 15 -7500 -3 22500 95625000025000 19 -2500 1 -2500 1625000030000 21 2500 3 7500 9625000035000 18 7500 0 0 05625000040000 20 12500 2 25000 415625000045000 23 17500 5 87500 2530625000050000 19 22500 1 22500 102506250000275000 180 355000 203 2062500000

From the above calculation, based on the same wayyielded the following equation:

Y = 15.25 + 0.0001X

So each time the amount of data increases the time theprocess will be 0.0001 times.

Based on the above regression calculation, it can be provedthat bDMQ not significant.bDMQ: bn = 0.0001: 0.0032bDMQ: bn H” 1:32

This shows that bDMQ not significant than bn. thus re-gression to remain non DMQ:

y = 35.5 + 0.0032x

whereas DMQ regression to be:

y = 15.25

B. Linear CorrelationThe term correlation refers to the concept of mutual rela-tionships between several variables. In correlation of thecomplex involving many variables at once. However, inthis discussion I take the two variables, namely the amountof data for the X and Y for the time. One formula to calcu-late the size of the correlation coefficient between two vari-ables that each scale intervals have been formulated byexperts and statistics formula called the product-momentcorrelation Pearson. Formula is as follows:

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To view without using the DMQ large amount of data tendsto be followed by the amount of time the process andreverse its increasingly small amount of data the smallerthe time the process is required. Of the changes in thevariable X is not followed by changes in the variable Y isabsolute. There is little variation shows that the largechanges in X are not always followed by a proportionalchange in the Y. This shows there is no indication thatperfect relationship between two variables and this is anon-physical characteristics of the variables. Relationshipthat can only be perfect once the variables science exactsciences.

The correlation is expressed in a number called the correla-tion coefficient rxy and given the symbol. Correlation co-efficients contains two meanings, that is a strong relation-ship and the direction of the weak relationship betweenvariables.

Strong, weak relationship between two variablesis shown by the large absolute price moves that correla-tion coefficients between 0 up to 1. The approximate num-ber 0 means the relationship is weak and the coefficientapproaching 1 means the number the stronger the rela-tionship.The data in Table 1. can be calculated linear correlationbetween the amount of data and the time to view the pro-cess without using the DMQ as follows:

= 0.97

While the linear correlation between the amount of dataand the time to view the process of using the DMQ-baseddata in the table 2. is as follows:

= 0.55

The high coefficient is defined as the existence of a strongrelationship between the amount of data with time. Posi-tive sign on the correlation coefficient shows that theamount of data that the higher the time the process has anincreasingly high.

C. Designing Through Program Flowchart

Figure 9. Register Value Flowchat GPA

D. Program Listing IPK is a list of values that a program using the methodDMQ (Query Data Mart), so that the listing program thatwill display the listing includes the list of update valuesGPA, and listing the value of a list GPA. Following theprogram listing:

<%dim connset conn=server.CreateObject(“ADODB.Connection”)conn.open “PROVIDER=MSDASQL;DRIVER={SQLSERVER};SERVER=rec;DATABASE=raharja_integrated;”%><% dim strsql1,rs1strsql1=”drop table Daftar_Nilai”set rs1=conn.execute(strsql1) %><% dim strsql2,rs2strsql2=”select * INTO Daftar_Nilai from Lap_KHS4"set rs2=conn.execute(strsql2) %><% response.redirect (“default.asp”) %>

Figure 10. Listing the value of the program update the listof GPA

V. IMPLEMENTATION

The concept of Data Mart Query (DMQ) was implementedon the Raharja University in List view to create value GPA(Cumulative Performance Index). GPA is an average of IPS(Index As Prestasi). IPK system is prepared to measureand know the level of ability for students to lecture. Usethe DMQ in the value of making a list of GPA, the appear-ance can be quickly accessed.

Figure 11. Query the database structure Raharja_Integrated

· Algorithm Lap_khs4:

SELECT dbo.Lap_Khs3.NIM, dbo.Lap_Khs3.Kode_MK,dbo.Lap_Khs3.Mata_Kuliah, dbo.Lap_Khs3.Sks,dbo.Lap_Khs3.Grade, dbo.Lap_Khs3.AM,dbo.Lap_Khs3.K, dbo.Lap_Khs3.M, DBo .QKurikulum.Kelompok, dbo.QKurikulum.Kajur FROMdbo.Lap_Khs3 inner JOIN dbo.Mahasiswa ONdbo.Lap_Khs3.NIM = dbo.Mahasiswa.NIM inner JOINdbo.QKurikulum ON dbo.Mahasiswa.Jenjang =dbo.QKurikulum.Jenjang AND DBo . Mahasiswa.Jurusan= dbo.QKurikulum.Jurusan ANDdbo.Mahasiswa.Konsentrasi =dbo.QKurikulum.Konsentrasi ANDdbo.Lap_Khs3.Kode_MK = dbo.QKurikulum.Kode

· The Screen

The screen (interface) Panel Chairman has beenintegrated with some system information such as RaharjaMultimedia Edutainment (RME), On-line attendance (AO),

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and Student Information Services (SIS). The interface - theinterface consists of:

a. Main Display Panel ChairmanOn this view we can see the entire of the Raharja univer-sity in the GPA (Cumulative Quality Index) and the IMK(Cumulative Performance Index). On this view there is alsothe number of students, men and women, along withstudent’s number, the student is entitled to UTS and UAS,Top 10 Best and Worst Top 10 GPA and IMK, and theaverage GPA for both active students and graduates fromRaharja university.

Figure 12. Main Display Panel Chairman

In the column on the left or top, when we click on the link-link, it will open a URL on the right-hand column. In thepicture above there is a link Asdir. When click on the link,it will open a URL that contains the entire Top 100 Stu-dents with Active status that are sorted descending. URLhas the interface as the image below.

Figure 13. Showing Top 100 Students with active status

In the above, there is a NIM, Student Name, and the GPAand the IMK. To be able to see the detail list of the GPA astudent, please click on the value of the student GPA.

b. Display the list of values on the GPA Board Panel

Unlike the previous interface, the interface panelat the GPA leaders illustrate the value of this special Cu-mulative Performance Index (GPA) of each student. GPA isthe average value of the overall value obtained in the wholesemester that has been executed by each student. IPK ispacked in a “Value List IPK” format that can be seen in theimage below:

Figure 14. List view GPA Value

To provide a list value above GPA, many tablesand queries used. So if using a conventional data sourcerequires a long time. However, the value of the display listis created with a GPA above the Query Data Mart, so that

at the time of opening this page, does not require a longtime.

VI. CONCLUSION

Based on the description above, concluded that the DataMart Queries (DMQ) is the appropriate method to speedup the process on a system with a database of informationdistributed. DMQ is used to avoid the use of complex que-ries. Thus DMQ will sacrificing the size of the data storagecapacity (space hard disc) to increase speed (increasespeed) in the initialization. This has proved both logic, thecalculation of graphs with linear regression and linear cor-relation and also through the implementation.

References

[1]Lin. J. L., Dunham M. H. and Nascimento M. A. A Sur-vey of Distributed Database Checkpointing. Texas:Department of computer science and engineering,Shoutern Methodist University. 1997.

[2]DeWitt. D.J., Gray.J. Parallel Database Systems: TheFuture of High Performance Database Processing.San Francisco: Computer Sciences Department, Uni-versity of Wisconsin. 1992.

[3]Mitchell Carolyn. Component of a distributed database.Department of Computer science, Norfolk state Uni-versity. 2004.

[4]Hamidah Ibrahim. Deriving Global And Local IntegrityRules For A Distributed Database. Departement ofComputer Science Faculty of Computer Science andInformation Technology, University Putra Malay-sia 43400 UPM Serdang. 2001.

[5]Steven P Coy. Security Implications of the Choice ofDistributed Database Management System Model:Relational Vs Object Oriented. University of Mary-land. 2008.

[6]Stephane Gangarski, Claudia Leon, Hurbert Naacke,Marta Rukoz and Pablo Santini. Integrity ConstraintChecking In Distributed Nested Transactions OverA Database Clustur. Laboratorie the InformationParis 6. University Pierre et Marie Curie 8 rue duCapitaine Scott, 75015, Paris. Centro de ComputacionParalela Y Distribuida, Universidad Central de Ven-ezuela. Apdo. 47002, Los Chaguaramos, 1041 A,Caracas, Venezuela. 2006.

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[7]Allison L. Powell, James C. French, Jamie Callan, Mar-garet Connell and Charles L. Viles. The Impact ofDatabase Selection on Distributed Searching. 23rdACM SIGIR Conference on Information Retrieval(SIGIR’00), pages 232-239, 2000.

[8]Huang Yin-Fu and JYH-CHEN HER. Fragment Alloca-tion in Distributed Database Design. NasionalYunlin University Sains and Teknology Yunlin. Tai-wan 640, R.O.C. 2001.

[9]Filipova Nadezhda and Filipov Filcho. Development OfDatabase For Distributed Information Measure-ment And Control System University of Economics.Varna, Bul. Kniaz Boris I. 2008.

[10]Stanchev Lubomir. Semantic Data Control In Distrib-uted Database Environment. University of Water-loo. 2001.

[11]Supranto, Statistik Teori dan Aplikasi, Erlangga, 2000.

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1. IntroductionThe information technology (IT) in the era melenium

at this time not only monopolized by businessorganizations. Governmental organizations, throughvarious ministries have also been using IT in order tooptimize the implementation of various activities.Government to use IT to support various areas of activitiesrelated to the public, asset management organization, theimplementation of aspects of service and operational, evento measure the achievement of the performance andefficiency. Through the application of IT, the governmentis expected to improve performance in various aspects inaccordance with the principles and way of goodgovernance, including: (1) create a governance ormanagement systems in good governance, (2) increasecommunity participation, (3) to know a complaint / realneeds of the community and follow up with the right /responsive, (4) created through the ease of gettinginformation, (5) increase public confidence in reciprocalback-the government, (6) accountability of the governmentconcerning the interests of the area, (7) the effectivenessand efficiency of service and operational activities ofgovernment (henderi et. all: 2008). Thus, the use of IT by

Paper

Information Technology Department – Faculty of Computer Study STMIK RaharjaJl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

email: [email protected]

AbstractMany government organizations have been using a lot of IT in the implementation activities. Utilization of IT is knownby the term e-government. Implementation in general aims to improve the quality of activities and services to thecommunity. Therefore, the role of e-government the more important to support and create good governance in thegovernment organization. Through e-government implementation of the government are expected to provide first-rateservice to the community. However, in fact the implementation of e-government is not everything went well in accordancewith the expected. The-efisienan to occur in the different practices of governance, development planning andimplementation of e-government has not been sustainable, the development of IT infrasrtuktur many overlapping, andservice to the community also has not been able to be either prime.

Keywords: IT governance, e-government, good governance

Saturday, August 8, 200916:50 - 17:10 Room L-211

IT Governance: A Recommended Strategy and Implementation Frameworkfor e-government in Indonesia

the government in general, aims to improve the quality ofthe activities and services to the community. Utilizationof IT by the government is more known as the e-government. Implementation of e-government is importantto realize the good governance, so that the government isable to provide the prime to the community. Because thegovernment has undertaken many efforts to implement e-government. Various application and infrastructure wasalso built and used in order to support its implementation.But in fact the implementation of e-government is noteverything went well and in accordance with the expected.In the implementation of e-government, the various-efisienan to occur in the practice of government activities,service to society can not be the prime, there are manyresources that terbuang useless, plan implementation anddevelopment of e-government is not yet sustainable,development and supporting IT infrastructure are alsomany overlapping. In others, the implementation of e-government supported IT governance is both veryimportant and are expected to have a significant thrust ofthe development. Therefore, in context development, ITgovernance is an issue which is very important because

Henderi, Maimunah, Asep Saefullah

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is one of the dimensions that determine the effectivenessand efficiency of IT utilization and ultimately determinethe success of the development. For that need to be effortsto improve the system of e-government through theapplication of the principles and way of working on ITgovernance.

2 IssuesBased on the facts mentioned above and some of

the results of research articles, opinions, and report onthe strategy, implementation and benefits of the use of e-government, it is known that the benefits of implementinge-government is not in accordance with what is expected,and not comparable with the value and investmentfinancing that has been remove. Implementation of e-government has not been able to significantly improvethe ease of public access in the information required, havenot been able to increase community empowerment, andnot play a role in providing maximum service to thecommunity. To overcome these problems, and thechallenges that have the necessary framework and ITgovernance strategy in e-government.

3. Discussion3.1 Definition of Research Literature on the Definition

of IT GovernanceAccording to Weill and Ross (2004) IT

governance is the authority and responsibility are properlyset in a decision to encourage the use of informationtechnology on the company. Meanwhile, henderi et. all(2008: 3) defines IT governance is the correct decision ina frame that can be responbility and the desire toencourage the use of information technology practices.On the other, Henderi et. all (2008: 3) also defines ITgovernance is the basis of the measure and decide theuse and utilization of information technology byconsidering the purpose, goals, targets and businesscompanies. Hence, IT governance is a business andsynergize the role of IT governance in achieving goalsand objectives of the organization and is the responsibilityof the Board of Directors and Executive Management. ITgovernance is also a fact on the control system of organsthrough the application of IT companies in order toachieve the objectives and targets set, create newopportunities, and the challenges that arise. Because ITis a governance structure and mechanisms that aredesigned to give the role of control and adequatemanagement for the organization or management. ITGovernance and management both have a strategicfunction, while management also have operationalfunctions. IT governance is the mechanism to deliver value,performance and risk management, with a focus on aspectsof how decisions are taken, who took the decision, thedecision of what, and why decisions are made. In the

context of governance, IT governance is an integral partof good governance. Following diagram is the theory ofIT governance architecture is briefly.

Figure 1. Diagram IT governance architecture theory

Base on architecture theory diagram IT governancein the image on the above, it can be concluded that ITgovernance in general, consists of four eleman, namely:1) the purpose/goal, 2) technology, 3) human/people, and4) process. Each of these elements must then beunderstood properly, the strategy defined achievements,and monitored the progress and Sustainability.

3.2 Definition of Research Literature The e-GovernmentE-government are often used by many, discussed

and reviewed in various forums and literature. However,understanding of e-government remain different. Forexample, Hazibuan A. Zainal (2002), quoted Heeks, definese-government as an activity undertaken by thegovernment to use information technology (IT) to provideservices to the community. This definition in line with theopinion Heeks stating that almost all governmentinstitutions in the world conduct activities and servicesto people with inefficient, especially in developingcountries. Many of the policy direction that governmentis not made well known by the general public, therefore,expenditure of funds are not reported well, and going onvarious queue service center is a supporter of a public-efisienan because the resources are terbuang.

3.3 Relations and IT Governance e-GovernmentApplication of the principles and way of IT

governance in e-government is an imperative and notdifficult to do because it has characteristics and goalsrelative to the same support the creation andimplementation of the principles and good governance isworking in different business organizations, social, andgovernance. In the context of the development andimplementation services to the community, principles andhow to work IT governance always have relationshipswith e-government. Because of the principles and way ofIT governance is to be applied to the e-government.

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Relations can accelerate the implementation ofdevelopment efforts in: (a) build the capacity of allelements of society and government in achieving theobjective (welfare, progress, independence, andcivilization), (b) community empowerment, and (c) theimplementation of good governance and public servicesare prime. The implementation and achievement ofdevelopment objectives can be optimized through theapplication of IT in e-governance tujan governance inaccordance with the characteristics of good governanceand. Here is the purpose of IT governance relationshipwith the goals and characteristics of good governance beachieved through the implementation of e-government(Henderi et.all: 2008, ):

Based on the above table in mind that the relationIT governance and e-government appears on the similaritywith the purpose of IT governance goals andcharacteristics of good governance be achieved withimplementing e-government. Through the implementationof IT governance in e-government and how the principlesof good governance can be optimized implementation.Therefore, e-government is built and developed withattention to and apply the principles and way of ITgovernance will result in e-government applications thatsupport the creation and implementation of most of theprinciples and main characteristics of good governance,including:1.Creating a governance system or organization in good

governance2. Increasing the involvement and role of community

(participatory)3.Improving the sensitivity of the organizers of the

government the aspirations of the community(responsive)

4.Ensure the provision of information and the ease ofobtaining accurate information and adequate(transparency) so that the trust created between thegovernment and the community

5.Provide the same opportunities for every member of thecommunity to increase welfare (equality)6.Guarantee the service to the community by using theavailable resources optimally and full responsibility ofthe (effective and efficient)

7.Increase the accountability of decision makers in all areasrelated to the broad interests of (the accountability)

In line with the relationships described above, eachstep of development undertaken by the government atthis time and the future challenges faced in the universalwhich can be briefly illustrated in Figure 2 below.

Table 1 Comparison of IT Governance Goals andObjectives with the Characteristics of Good Governance

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Figure 2. Development Scheme in the Knowledge Era(Taufik: 2007)

Based on the two pictures above, the development donein the era of the knowledge and application of ITgovernance in e-government should consider some basicthoughts below:1.Primary goal development needs to be translated

through the increased competitiveness of the nationand social regulation. Development as a process ofdevelopment must be concrete can improve the welfareof a higher, more equitable, strengthen self-reliance, andpromoting civilization. Implementation of IT governancein e-government can help accelerate the achievementof this goal.

2.Otherwise have the best potential and uniquecharacteristics, the implementation of development ateconomic network, and (5) of the locality.

3. competitiveness and social regulation. 4. ICT is also one of the factors are very important in the

context of development undertaken by the government.

Based on the relation of thought and above,implementation of IT governance in the context of e-government is relevant to optimize the implementation ofthe principles and way of good governance of government.Thus, the step to bring development and pendayagunaane-government that is supported by the principles and howto work a part of the IT governance is important inoptimizing the implementation of the development to bemore effective, efficient and empowering communities.

3.4 IT Governance Requirements in e-GovernmentIT Governance needs in e-government in line with

the terms and understanding of good governance is oftenused approximately 15 years after the last internationalagencies condition good governance in the aid program.In the field of science information technology, goodgovernance, known by the term IT governance. Thus theneed for IT governance in e-government is an equity inorder to optimize the role of IT to achieve good governancein the organization of government. Through theimplementation of IT governance in e-government and

good governance principles for the organization ofgovernment can be implemented in the form ofstrengthening transparency and accountability,strengthening the regulation (setting, supervision pruden,risk management, and enforcment), the integrity of themarket to encourage, strengthen cooperation, andinstitutional reform. Therefore, IT governance needs in e-government is also analogous to the definition of ITgovernance in the framework of governance given by Rosset. All (2004) is that IT governance as a set of management,planning and performance reports, and review processesassociated with the decisions correct and appropriate,establish control, and measurement of performance onthe key investors, services, operations, delivery andauthorization to change or new opportunities appropriateregulations, laws and policies. In connection with theabove, e-government should be built , and implementedin accordance with this principle and how to work ITgovernance, involving elements of decision-makers ingovernment, well planned, decided to consider decision-making processes are correct and appropriate (consensus),controlled and monitored the development andimplementation, evaluation of achivement in improvingthe quality of development and first-rate service to thecommunity, creating new opportunities and the challengesthat arise, and it matches with the regulations and policies-government policy and even the global rules. Meanwhile,in the context of government, far as this legal foundationfooting form new IT governance regulations in line withthe principles of good governance is more emphasized inthe context of practices of corruption, misuse of fundsand anticipate the country. In its development, efforts toreform the bureaucracy and the development of goodgovernance system of government continues to beimproved. Although some government agencies have adocument that already have IT master plan, but oftendifficult implement IT in the form of good governance todevelop e-government with a variety of reasons. Becauseof improvements in the level regulation is important isdone through creating a framework and strategies for ITgovernance to the development of e-government.Framework and that this strategy must be accompaniedwith the seriousness, consistency and the various capacitybuilding, particularly the government to achieve good ITgovernance in e-government. Framerwork strategy and isexpected to bring to the action plan, and the implementationof IT governance in e-government in accordance with theneeds, abilities, specific level of government organization,and can support the implementation of the principles ofgood governance and good governance in the context ofIT development.

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3.5 Infrastructure and implementation of IT Governancein the e-Government

3.5.1 Infrastructure

Provision of information and communicationinfrastructure, information communicatioan andtechonoloy (ICT) are integrated effectively and efficientlyin different levels of government are very important andclosely related to IT governance, governance anddevelopment. ICT infrastructure to be developed andplanned development. Because if this is ignored will leadto the occurrence of waste ICT infrastructure development.When this waste is going because its development is oftennot synchronized with each other, and often overlapping.Government agencies often work alone withoutconsidering the efforts made by other parties. There is anIT governance framework is expected to create a nationalinformation infrastructure and integrated communications.

3.5.2 Implementation of IT Governance in the E-Government

Problems with the elements of ICT infrastructure,as described previously is a result of implementation notwork and how the concept of IT governance in thedevelopment and implementation of various e-government. While the development and empowering e-government is important to realize good governancesupport, and provide public services to the people of theprime. However, implementation of e-government withoutimprovement followed by the IT governance in thegovernment sector and how the principles of goodgovernance in the system of government will not createan optimal, even not possible will only be for thegovernment discourse. Therefore, the concept of workand prinisp IT governance implicitly applied in e-government.

4. IT Governance Framework Strategy for e-Government Strategy Framework4.1 Many IT Governance IT governance framework strategy that has been made.

Some of them were prepared and issued by theIT Governance Institute and COBIT. From the variousframework and strategy, conclusions can be drawn thatthe framework and strategies for IT governance in generalconsists of two elements of the IT governance domainsand objective. Framework and IT governance strategy thatcan be referred to briefly illustrated in the form of ITgovernance life cycle as follows.

Figure 3. IT Governance Life Cycle-Static View (ITGovernance Implementatin Guide, 2003)

Based on the three images above, note that thedomain of IT governance is part strategy implementationconsists of five main components (Erik Guldentops: 2004),namely: (1) Aligment, (2) Value delivery, (3) Riskmanagement, (4) Resource management, (5) Performanemanagement. Explanation of the five main componentsare as follows:1. Aligment; emphasized the integration of IT

organizations to enterprise organizations in the future,and IT operations that are run in order to create addedvalue for the organization.

2. Value delivery; the value of IT through the creation ofthe accuracy of the time in completing the projectconstruction, the accuracy of the budget is used, andthe needs that have been identified. The process ofdevelopment must be in IT-design, improved andoperated efficiently and effectively through theaccuracy of the settlement and achievement ofobjectives set and agreed.

3. Risk management; Value includes the maintenance,the internal control to test enterprise governanceorganization to stakeholders, customers and keystakeholders to improve the activity management ofthe organization.

4.Resource management; regarding the establishment anddevelopment of IT capabilities that fit well with theneeds of the organization

5.Performane management; cycle includes the provisionof clear feedback, IT governance initiatives aligmenton the right path, and create new opportunities withmeasurement is correct and timely.

IT governance strategy that is described based onfive main elements of the above, in line with the objectivesbe achieved by the leaders of the organization in the fieldof IT investment. Some goals are:

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a.For make cut operational costs or increase fees and cutcosts the same. For example, the marketing costs thatmust be issued by the company (goals transactional)

b.For make meet or provide information to various needs.Including financial information, management, control,report, communicate, collaborate, and analyze(destination information)

c.For make benefit or competitive positioning in the market(market share increase organization). For example, thevarious facilities provided by banks has changed thestatus of savings products that become a form ofinvestment for bank customers at this time to be aproduct of the bank must be paid by the customer(strategic goal).

d.Delivery base/foundation on the services IT by differentapplications, such as servers, laptops, network,custmoers database, and others (infrastructurepurposes).

4.2 Framework e-Government StrategyFramework e-government strategy is basically

defined as a blue print on the IT aspects of the developmentand implementation service to the community to be moreeffective, efficient, and empowering the community. Inconnection with the case, the framework e-governmentstrategy is in essence a framework of a generaldevelopment strategy and how useful e-government inthe cycle of planning, implementation, monitoring,evaluation and continuous improvement in order tobecome an integral part of development and service tothe community. Framework strategy e-government alsohas a meaning that the development and services that thegovernment needs to do more useful ICT in realizing thegoals.

Framework of this strategy can become acomprehensive guide and ready to use and tailored to thenature and characteristics of development and servicesmade from time to time. Thus, the follow-up plan can betranslated in accordance with the challenges, skills andgovernment priorities. Aligment unison and action can beexpected to provide adequate support for the change, andmay trigger a significant impact in implementing andachieving the goal. Framework e-government strategy toprovide philosophical foundations for IT implementationin governance and development services in accordancewith the principles of good governance. Framework e-government strategy is a minimal-consisting of theelements essential element following:1.Leadership (e-leadership), policy and institutional;2.Information and communication infrastructure/ICT

integrated3.Application of ICT in government (e-government)4.Utilization of ICT in community development (e-society);

and

5.Industrial development and utilization of ICT forbusiness (e-business)

From the five main elements of the e-governmentstrategy, the elements of leadership should be and how toapply the principles of e-leadership. Elements of e-leaderhsip this line with the opinion delivered by Henderi,et. All (2008: 165) that the manager at this time theorganization is required to understand the concept andhow to work the implementation of information technologyto support the implementation of the functions ofleadership. Elements of e-leadership is applied inpreparing, implementing, and evaluating the policies,programs, and services performed. Therefore, the mainelements of this are affecting the other elements in theframework and strategies for e-govenment. In line withthe fifth framework and main elements of e-governmentstrategy mentioned above, several other principal termsalso play a role in encouraging the implementation of ITgovernance in e-government, including:a.IT governance should be an integral part of the

governance system of government.b.Many determines policy and the main stakeholders need

to create harmony (alignment) between the ICTdevelopment and services performed.

c.Aligment context with ICT organizations or systems ofgovernment regulation requiring the implementation ofthe framework strategy and appropriate, especially inthe implementation phase.

Therefore, IT governance framework in thedevelopment, implementation and development of e-government is absolutely prepared, approved, certified,and have an adequate legal basis so that they cancontribute significantly in optimizing the development andimplementation of the prime ministry to the community inaccordance with the principles of good governance.

5 ConclusionGovernmental organizations, through various

ministries and instansinya utilize IT in order to optimizethe implementation of various development activities andservices to the community. Utilization of IT by governmentare known by the term e-government, the implementationis very important to support and achieve good governance,so that the government is able to provide the prime to thecommunity. For that, IT governance framework strategyin e-government is made absolute and is used to preventor reduce the various permasalaan faced in theimplementation of e-government at this time such as: theefisienan-going activities in different practices ofgovernance, resources spent useless, various e-development plan government has not been sustainable,the development of IT infrasrtuktur many overlapping,and services to society can not be either prime. This articlerecommends a strategy framework for the development of

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IT governance, development and implementation of e-government with the main attention to the five elementsof IT governance, namely: (1) Aligment, (2) Value delivery,(3) Risk management, (4) Resource management, and (5)Performane management, and further define the fiveessential elements of e-government strategies, namely:(1) Leadership (e-leadership), policy and institutional, (2)Information and communication infrastructure, integrated,(3) The application of ICT in government (e-government),(4) Utilization of ICT in community development (e-society), and (5) Development of ICT industry and theutilization of business (e-business). Framework, and thisstrategy can further be a reference in the developmentand implementation of e-government can accelerate theimplementation of the principles of good governance inthe system of government.

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Henderi and Padeli (2008). IT Governance - Support forGood Governance. CCIT Journal 2 (2), 142-151.

Henderi, Maimunah, and Euis Siti Nur Aisyah (2008). E-Leadership: The concept and the impact onLeadership Effectiveness. CCIT Journal 1 (2), 165-172.

Kordel Luc (2004). IT Governance Hands-on: Using Cobitto implement IT Governane. Informatioan SystemControl Journal, Volume 2, USA

Ross, Jeanne, and Weill, Peter. (2004). Recipe for GoodGovernance, CIO Magazine, 15 June 2004, 17, (17).

Taufik, (2007). IT Governance: The approach to realizeintegration in the development of Regional TIK.Seminar Materials Trisakti University, Jakarta.

Anonymous (2007). What is Good Governance? UnitedNations Economic and Social Commission for Asiaand the Pacific (UN ESCAP) (4 pages). Accessedon 30 January 2009 from: http://www.unescap.org/huset/gg/governance.htm

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I. FOREWORDS1.1 Background

Lecturers are one among essential components ineducation system in university. Roles, assignments, andlecturter’s responsibility is very important in realizingnational education. To perform these functions, roles, andstrategic position, it is required professional lecturers.Human Resources for Lecturers has vital position increating quality image of then graduates as well as qualityof institution in general. This position should also bestrengthen by the fact that lecturers should haveconsiderable authoritiesin academical process, and evenhigher than similar proffesion in lower educationalinstitutes.

People demand quantity and quality of the outputgenerated by University / Faculties / Majors is growingstronger. Eventhough numbers of graduates fromuniversity much larger than previous, particular majorsand especially in terms of quality still below expectation.More advance the civilization, greater the competion invarious fields, including fields of science and technoilogy,

Paper

Program Study Information TechnologyUniversity Paramadina

Email : [email protected]

AbstrakSince 2008 Indonesian Governmemt had begun to conduct certification for lecturers of non professor with

purpose of recognation of profesionalism, profession protection and tutor welfare in Indonesia. All these efforts areexpected, at the end, could improve quality of mutu colleges and university in Indonesia, as lecturesrs are mostimportant component and determining in studying and teaching in university.

The fact that is not easy for lecturers to be certified, besides limited quota, the process should go throughcomplex bureaucracy. Threfore every university sahould set up mechanism and strategy in order to simplify forgaining certificate for the lecturers. Among other thing that may be done is to set up integrated information system tosupport process for certification for the lecturers. Required stages are process design model, application modulesdevelopment and implementation also system testing. Result of this investigation copuld become reference for everyuniverity in Indonesia for dalam accomplishment of certified lecturers.

Key words: information system, certified lecturers

Saturday, August 8, 200915:10 - 15:30 Room L-211

Information System DevelopmentFor Conducting Certification for Lecturers in Indonesia

furthermore in globalization. Univeristies had becomingpeoples’s foothold in generating highh quality humanresources. Lecturers empowerment is becomingcompulsary for universities, as it is key success University/ Faculties / Majors, where 60% of university is in handsof these lecturers. Whatever education enhancementpolicies were designed and defined, at the end of the day,lecturers who are actually performing in teaching activities.Learning and teaching activities are very much dependingon lecturer’s competence and commitment.

Lecturers professionalism valuation is cruciallyimportant to be performed as one of the effort to enhanceeductaion quality in high education system.Proffesionalism recognation may be realized in the formof granting certificate to lecturers by authorizedestablishement. Certificate awarding is also effort tomconatitute profession protection dan warranty of lecturerswelfare. Since 2008, Indonesian government haveconducted certification process for lecturers non-

Yeni Nuraeni

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proffesor based on PERMENDINAS 42 of 2007 (Decree #420 Ministry of National Education) as effort to havepatronage of lecturers in performing professionalassignment.

Execution of certification for lecturers nowadaysperformed by certification establishment in universitiesand or in cooperation with other university as accrditedadministrator of certification. Every university privatemanaged as well as state own in Indonesia were guided toempowering existing unit or to build a new one which haspotency to perform certification program. Every universityrequire strategy to make effort for all lecturers undercampus community will be certified as one of efforts tohave performance enhanced and lecturers welfare dosenwhere ultimately expected to implicate on educationquality enhancement.

Based on above condition, therfore in this researchwill build an application functioned to facilitateaccomplishment process, monitoring, evaluate and toguide lecturers certification in university. Withnthisapplication, it is expected every university in Indonesiacana accommodate bigger opportunity dan faster for thelecturers to obtain certification.

1.2 ObjectiveThis study has specific objective to perform the following:1. Analyze certification process where so far had been

conducted dan identified problems (non added valueprocess) which had resulted certificatio processbecoming ineffective and inefficient.

2. Developing process model for certifcation for thelecturers according to the need, certificationcommittee, PTP – Serdos and other related parties.

3. Developing application, monitoring, evaluating andguiding lecturers certification according to suggestedprocess model.

4. Take reference material in conducting certificationprocess for each university in Indonesia.

1.3 Urgency for ReseachLecturers certification process had been involvingbureaucracy procedure with many institutions beinginvolved. Parties who were involved in the process wereconcerned lecturer, faculty / majors / prodi, students,colleagues, direct superior, serdos committee on universityproposer, university conducting lecturer certification (PTPserdos), Directorate General of High Education, andPrivate Universities Co-ordinator. This process is relatedto bundle tracker, required files and documents forprocessing certification generally still be done manually.

This causing the certification process lengthly dancomplicated.

Beside problem in certification processing, there isalso quota problem which limiting number of lecturersbeing recommended to have them certifioed. Governmenthad defined quota number of lecturers who cabn beproposed to have them certified for each university inIndonesia. Becase of this quita, every university has tohave strategy and mechanism to define first prioritizedlecturers to be recommended in getting certificated everyyear and ensuring those recommended lecturers have bigchances to pass the test and getting certificates.

Based on those problems, it is required to analizeaccomplishment process for lecturers in gettingcertificates to find out non-added value process whichresulting ineffective and unefficien for lecturers in gettingcertificates, furthermore based on the analysis ; processmodel will be made which hopefully can elliminate all thosenon-added value processes into added value processes,it will then be followed up by contructing accomplishmentprocess implementation for lecturers in getting certificates.

With this new process model and implementiing it, wehope the following :1. Can motivate lecturers to improve their

profesionalism and getting recognition and rightaccording to government policy as stated inPERMENDINAS 42 of 2007

2. Simplify certification process for lecturers, which involving internal parties and external university

proposer.3 University proposer has strategy and mechanism to

utilize lecturer quota whio can be recommendedtobe certified, so that every lecturer has impartialityand big opportunity to be certified in accordancewith his / her academic quality, performance,compentency and contribution.

4. Dengan mendapatkan sertifikasi maka kesejahteraandosen akan bertambah dan diharapkan akanberimplikasi pada peningkatan kinerja sertaperbaikan mutu perguruan tinggi di Indonesia.

5. University has program planning for councelingthose lecturere who do not have certificate andprogram planning for quality assurance of certifiedlecturer so they can always strive for selfimprovement in encounter challenging new Scienceand Teknology.

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II. LITERATURE REVIEW2.1 Concept of Lecturer Certification in Indonesia As Sebagaimana stated in Statute number 14 of 2005

concerning Teacher and Lecturer, lecturers expressedas proffesional educatorand scientist with mainassignment to transform, develop, and wide-spreadsciencem technology, and art througheducation, risearch and dedication to the society(Chapter 1 Article 1 verse 2). Meanwhile, proffesionalare also expressed as proffesion or activities doen bysomeone and as earning that require expertise, skill,meet quality standard of particular norm withprofession education.

Academic qualification for lecturer and various aspects of performance as defined in SK Menkowasbangpan Nomor 38 Tahun 1999 (Decree of Ministry Coordination of wasbangpan), denoted as determined aspect of lecturer authority to give lecture in particular stage of education.Beside, competency of the lecturer is also determining requirement for teaching authority.

Teaching Competency, particularly lecturer,accounted as set of knowledge, skill dan bahaviourthat should be possesed, mastered and realized bylecturer in performing his / her professionalassignment. These competencies including pedagogycompetency, personality competency, socialcompetency and professional copentency.Lecturer competency define performance quality ofTridharma College as shown in professional lectureractivities. Competence lecturer to execute theirassignment professionally are those who havepedagogic competence, proffesional competence,proffesional competence, personality competence,and social competence required for education,research practices, and dedication to the society.Students, colleagues and superiors can value level ofcompetence of lecturers. Because of the valuation isbased on perception during interaction betweenlecturers and evaluators, thus this valuation may becalled as perception valuation.

Academic qualification and performance, level ofcompetence as valuated by others and by themselves,and contribution statement by themselves, alltogether will define lecturer professionalism.Professionalism of a lecturer and his / her teachingauthority stated in presenting teaching certificate. Asappreciation for being professional lecturer,government provide various allowances and alsoprofessionalism related matters of a lecturer.Certification concept briefly presented in scheme onFigure 2.1.

Lecturer certification procedure can be illustrated asfollows :

Figure 2.2 : Procedure Lecturer cirtification

2.2 Lecturer Certification Unit of Quality Assurance

Directorate General of High Education carry outmonitoring and evaluating through Quality AssuranceUnit in ad hoc manner. based on the result of monitoringand evaluating towards PTP-Serdos Quality AssuranceUnit, giving recommendation to Director General of HighEducation concerning status of PTP-Serdos. QualityAssurance Unit of internal university conductingmonitoring and evaluation towards certificationestablshment in related university. Performance of InternalQuality Assurance being monitored and evaluated byQuality Assurance Unit for High Education.

Lecturer Certification is meant for getting theachingauthority in college and university in accodance withLegislation No. 14 of 2005. Obvious challenge is challengeof IPTEKS development challenge in real life. Lecturers incolleges and universities should always be able to improvequality of themselves be aup against this challenge.Quality assurance program post certification in facingIPTEKS development:1. Continual counceling by internal university as well

as other institution.2. Self studi being performed by lecturers individually

as well as a group.3. Application concept of life long education where

study is part of their life.

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Figure 2.3 : Quality Assurance Unit of LecturersCertification

III. METHOD OF STUDY

Taken steps for development of information system oncertification for lecturer in Indonesia are as follows :

1. Requirement Analysis On this stage it is performedliterature study and analysis concerning policies,valuation system and creterion being applied, allinvolved parties in lecturers certification alh occurredproblems identifation pon the execution.

2. Lecturer Certification Design Process ModelOn this stage it is performed process model forexecution of lecturers ertification with considerationof efficience aspects dan maximum possible toeliminate manual processes. On designing thisprocess it is made an internal certification process,performed by proposer university and an externalcertification in coordination with authorized parties(PTP serdos, kopertis and Ditjen Dikti)

3. Designing process of Information sytemArchitecture data à that is defining all required data,where the position is and how to access them.Software architecture à on this stage, it is definedsoftwares to be used, whatever application will bebuilt using this particular software, whateverfunctions will be used dan also how to utilize andretrieve them.Appearance architecture à on this stage, it isdefined lay out design and the look or appearance.Infrastucture architecture à defining server to hostthe website, where software can be run, and whatcomputer platform will be used .

4. Implementation and calibration will consist of thefollowing steps:- Build and calibrate application codes and

functions to be used.- Installing requitred infrastructure components- Installing and running the system

IV. RESULT AND DISCUSSION

4.1 Execution of Lecturer Certification Process in Indonesia

Provcess model consists of three stages i.e:1. Internal certification process model

An internal certification process is conducted byproposer universitywith target of selecting lecturereswhich will be recommended to receive an externalcertificates. On this stage, it is performed scorecalculation stimulation of the professionalism basedon th existing criterion, for passed lectureres willcontinue to be proposed for an external certification,where for those who can bot pass the test will receivecounceling program, and may be recommendedreplacement lecturers to fill up government quota.:

2. External Certification Process ModelFor lecturers who have passed an internal certificationprocess will continue to follow an external certificationprocess in accodance with defined procedures bygovernment.

3. Counceling Process Model and Quality Assurancefor Lecturers CertificationThose lecturers who could not pass both internal andexternal, will be conducted a conceling to enhancetheir professionalism, in oder to be re-proposed fornext opportunities of sehingga pada kesempatanmendatang dapat diajukan kembali untuk attend anexternal certification. For those lecturers who havepassed certification will be performed qualityassurance thus lecturers may continously enhancingthemselves in according to Science and Technology.

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4.2 Software Module of Information System for Execution of Lectuirers Certification in Indonesia

Based on above mentioned process modeldesign, it is required number of application modules asfollows:1. Data application of academical lecturers à to store

academic position data, education stepladder andsequence list of lecturers grades

2. DSS aplication & Expert System à to definelectureres priorities dosen to be proposed forcertification and PAK score counting of eachlecturer

3. Perceptional valuation application à consist ofquestioners form which should be filled up bystudents, co-workers and selected superior toconduct perceptional score counting.

4. Tridarma contribution of Lecturer application à inform of portofolio which should be filled up byproposed lecturer to attend certification andconduct personal score counting.

5. Combined scores application à score counting oncombined PAK and Personal scores.

6. Consistence application à counting consistencybetween perceptional consistence score andpersonal scores.

Figure 4.1: execution of Lecturers Certification in Indonesia

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7. Passing Definition of Lecturer CertificationApplication à counting score final marks danprovide lecturers lists of passed and failed in aninternal certification process.

8. Lecturere counceling program application à to usedfor program planning of lecturer counceling who donot pass both internal and external certification.

9. Aplikasi program penjaminan mutu dosen sertifikasià untuk merencanakan program pembinaanberkelanjutan, studi mandiri dan life long educationbagi dosen yang sudah mendapatkan sertifikasi.

4.3 User Interface of Information System forConducting Lecturers Certification in Indonesia.

Several user interface from information system onexecution of lecturer certification may be seen on nthefollowing Illustration:

Figure 4.2: User Interface from information system onexecution of lecturer certification

V. CONCLUSION

Lecturers are most important component ineducation. Lecturer in the position as professionaleducators and assigened scientist to be able intranforming, developingand and wide-spread-out sience,technology dan art vthrough education, research anddedication towards people. Government of Indonesiathrough Permendiknas no 42/2007 awarding recognitiontowards professionalisme, protect profesion and asloassuring lecturers welfare in the form of execution oflecturer’s certification..

Each and every college and university in Indonesiashould have mekanism dan efective strategy in executinglecturers certification for ensuring lecturers under shelterof their organization can easily obtain certfication thusexpect performance and welfare of lecturers may beimproved and ultimately can be implicated on qualityenhancement of univerities in Indonesia.

VI. REFERENCE LIST

1. Direktorat Jenderal Pendidikan Tinggi, DepartemanPendidikan Nasional, 2008, Buku I Naskah AkademikSertifikasi Dosen, Ditjen Dikti

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2. Direktorat Jenderal Pendidikan Tinggi, DepartemanPendidikan Nasional, 2008, Buku II PenyusunanPortofolio Sertifikasi Dosen, Ditjen Dikti

3. Direktorat Jenderal Pendidikan Tinggi, DepartemanPendidikan Nasional, 2008, Buku III ManajemenPelaksanaan Sertifikasi Dosen dan PengelolaanData, Ditjen Dikti

4. Ditjen Dikti,2008, Kinerja Dosen Sebagai PenentuMutu Pendidikan Tinggi, Ditjen Dikti

5. Tim Sertifikasi Ditjen Dikti, 2008, Sertifikasi DosenTahun 2008, Ditjen Dikti

6. Tim Serdos UPI, 2008, Sosialisasi Sertifikasi Dosen,Tim Serdos UPI

7. Barizi,2008, Pemberdayaan dan Pengembangan KarirDosen, Institut Pertanian Bogor

8. Pressman , Roger S, 2007, Softaware Engineering: APractitioner’s Approach, McGrawhill Companies, Inc.

9. Suryanto Herman Asep, 2005, Review MetodologiPengembangan Perangkat Lunak, http: www.asephs.web.ugm.ac.id

10. Dadan Umar Daihani, 2001, KomputerissiPengambilan Keputusan, PT.Elex MediaKomputindo, Jakarta.

11. Turban Efraim, 2005 Decision Support Systems AndIntelligent Systems, Edisi 7 Jilid 1 & 2, Andi ,Yogyakarta

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Paper

Doctoral Program Student in Computer Science University of Indonesiaemail: [email protected]

ABSTRACTThis study describes a model of adaptive e-learning system based on the characteristics of student’s motivation in thelearning process. This proposed model aims to accommodate learning materials that are adaptive and intelligent basedon the difference of the characteristics of student’s motivation, and to solve problems in a traditional learning system,which only provide the materials for all students, regardless of motivation characteristics. Thus, the student must beprovided with good materials in accordance with the ability to study their characteristics and also their motivation inlearning. Approaches to the system development consist of have advantages in terms of combining the characteristics ofmotivation, intelligent systems and adaptive systems, in e-learning environment. The system becomes a model forintelligent and adaptive e-learning diversity of student’s motivation to learn. Study will identify student’s motivation inlearning with the support of intelligent and adaptive system, which can be use as a standard for providing learningmaterial to students based on the motivation levels of students.

Keywords: adaptive system, e-learning, studen’s motivation, adaptive e-learning.

Saturday, August 8, 200915:10 - 15:30 Room L-211

A MODEL OF ADAPTIVE E-LEARNING SYSTEMBASED ON STUDENT’S MOTIVATION

Sfenrianto

1. INTRODUCTIONThe development of e-learning activities in universitiesshould be able to observe the condition of the studentconcerned, due to the changes in paradigm of learningthat is from Teacher Center Learning toward StudentCentered learning. System e-learning approach with theStudent Centered Learning can encourage students tolearn more active, independent, according to the style oflearning and others. Thus, by using e-learning systemapproach with the Student Centered Learning will be ableto support students to learn more optimal because theywill get the learning materials and information needed.E-learning system should be developed to be a studentcentered e-learning. The system will have various featuresthat can support the creation of an electronic learningenvironment. Among other features to ensure interactionbetween faculty with students, to determine the variousfeatures of the meeting schedule and the assignment forboth tests, quizzes, and writing papers [1].E-learning in universities at this time is not fully StudentCentered Learning, and limited only to enrich the teachingof conventional [1].

Conventional learning is the same learning materials foreach user as it assumes that all characteristics of the useris homogeneous. In fact, each user has characteristicsthat differ both in terms of level of ability, motivation,learning style, background and other characteristics. e-learning system should be developed to overcome theconventional learning, in terms of providing learningmaterials and user behavior, particularly for theclassification of student’s motivation.

Motivation is a paramount factor to student 2success. In particular, many educational psychologistsemphasize that motivation is one of the most importantaffective aspects in the learning process [2]. From thecognitive viewpoint, the motivation is related to how anindividual’s internal student such as goals, beliefs, andemotions affects behaviors [3]. Motivation obviouslyinfluences on students learning behaviors to attain theirlearning goals. Therefore, one of the main concerns ineducation should be how to induce the cognitive andemotional states and desirable learning behaviors whichmake the learning experience more interesting [4]. Although

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it is well known that the student’s motivation and emotionalstate in educational contexts are very important, they havenot been fully used in e-learning base on motivationstudent characteristics in the learning.An AES (Adaptive E-learning Systems) try to developstudent centered e-learning method, with therepresentation of learning content that can be customizedwith different variations of the characteristics of userssuch as user motivation. An AES can gather informationfrom users on the objectives, options and knowledge, andthen adapt to the needs of specific users. This is becausethe components can be developed from the AEScombination two systems, namely: ITS (IntelligentTutoring Systems) and AHS (Adaptive HypermediaSystems) [5].ITS components can consider a student’s ability, presentthe material in accordance with the learning ability andmotivation characteristics of students’s.This way the learning process becomes more effective.Ability to understand the student is part of the“intelligences” ITS, other than that ITS can also know theweakness of students, so that decisions an be taken toaddress them pedagogic [6]. While the AHS components,can then designed an elearning material delivered tostudents dynamically based on the level of knowledgeand material format in accordance with the characteristicsof motivation. In other words, the AHS provides a link /hyperlink in the display to navigate to relevant informationand hides information that is not relevant. Then there isthe freedom, flexibility and comfort of the students toselect appropriate learning material desires [5].This allows the student’s of increased motivation in usingelearning.

2. ADAPTIVE E-LEARNING SYSTEM

Many e-learning systems developed to support studentsin learning. E-learning will be able to support them to getthe learning materials and information needed. One methodthat can be used in optimizing the effectiveness of thelearning process through E-Learninga as an adaptivesystem [3].

Therefore, an adaptive system for e-learning is called anadaptive e-learning system (AES).An adaptive e-learning system is described, according toStoyanov and Kirschner, as follows:“An adaptive e-learning system is an interactive systemthat personalizes and adapts e-learning content,pedagogical models, and interactions betweenparticipants in the environment to meet the individualneeds and preferences of users if and when they arise”[7]. Thus, an adaptive e-learning system takes all propertiesof adaptive systems. To fit the needs for the application in

the field of elearning, adaptive e-learning systems adaptthe learning material by using user models.E-learning system is called adaptive when the system isable to adjust automatically to the user based onassumptions about the user [7]. In the context of e-learning, adaptive system specifically 3 focused on theadaptation of learning content (adaptation of learningcontent) and presentation of learning content(presentation of learning content).

How the Learning content to presented, the focus of anadaptive system [8]. Then De Bra Et Al. propose a modelof adaptive system consists of three main componentsnamely: Adaption Model, Domain Model and User Model,such as the following figure: [9] Figure 1. A Model ofAdaptive System [9] Then Brusilovsky and Maybury,propose An adaptive system consists of the learner andthe learner’s model. As the following figure: [10]:Figure 2. An Adaptive Systems [10] The explanation ofthe adaptive system, then there can be three-stageadaptive system process, namely:the process of collecting data about propile user LearnerProfile), the process of building a model user (UserModeling) and the process of adaptation (Adaptation).This study will describe the details of each sub-model ofadaptive system in the following 3 (three) sections.

2.1 Learner ProfileIn adaptive system, learner profile components use toobtain student information. This information is storedwithout making changes, and does not close thepossibility of changing information. Changes occurbecause learner profile information such as: level ofmotivation, learning style, and others also change. Thelearner profile has four categories of information that canbe used as benchmarks, namely: [11].· Studen’s behavior, consists of information: level of

motivation, learning style and learning materials.· Student’s knowledge, the information about the

knowledge levels of students. There are twoapproaches that can be used, namely: the testautomatically (auto-evaluation) by an adaptivesystem and the test manual (manualevaluation) by ateacher.Levelsofknowledge students can becategories: new, beginner, medium, advance and expert.

· Student’s achievement, the information relate tostudent achievement results.

· Student’s preferences, which explain the concept ofinformation preferences, such as:cognitive preferences: (introduction, content, exercise,etc.), preferences physical support (text, video, images,etc.).

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2.2 User ModellingThe user modeling process requires techniques to gatherrelevant information about the student.Therefore, it is plays an important role in adaptive system.According Koch, The purposes of user modeling are toassist a user during learning of a 4 given topic, to offerinformation adjust to the user, to adapt the interface tothe user, to help a user find information, to give to theuser feedback about her knowledge, to supportcollaborative work, and to give assistance in the use ofthe system. [12].

Then De Bra Et Al. also describes the goals of usermodeling are to help a user during a lesson given topic, tooffer customized information to users, to adjust the linkinformation to the user, to help a user find information, toprovide feedback to the user’s knowledge about, tosupport collaborative work, and to provide assistance inthe use of the system [9].

2.3 Adaptation ModelThe process of adaptation is develope to student’sachievement, student’s preferences, student’s motivation,and Student’s knowledge. These benchmarks of thestudent are stored in a user modeling. The user modelingis hold by the system and provides information about theuser like for example, knowledge, motivation, etc. A usermodeling gives the possibility to distinguish betweenstudent and provides the system with the ability to tailorits reaction depending on the modeling of the user [10].Thus, an Adaptation model is require by the adaptivesystem to get information on adaptive. It is contains a setof adaptation rules that exist in the stated terms andconditions of an action from the adaptive system.System components for adaptive e-learning can bedeveloped, as follows:[13]

· Adaptive information resources, give the userinformation in accordance with the project they aredoing, add a note with the resources and projects thatneed to be based on knowledge of the user itself.

· Adaptive navigational structure, record structure, oradapt to provide navigation information to users aboutlearning the material in accordance with the next lesson.

· Adaptive trail generation, provides some guidance bygiving examples that fit with the goals of learning.

· Adaptive project selection, provides a suitable projectthat depends on the user’s goals and previousknowledge.

· Adaptive goal selection, suggested that the purposeof learning the knowledge of the user.An adaptive e-learning system (AES) that has beenpreviously described, component’s AES can bedeveloped. In AES development components such as:

learner propile, and user modeling will allow for thecombination with the characteristics of student’smotivation, and information focus on student’smotivation and student’s behavior.Next topic will explain the two main components thatsupport AES. AES is a combination of two components:intelligent tutoring systems (ITS) and hypermediaadaptive systems (AHS). Such as the following figure3: Three main components, namely: ITS and AHS, suchas the following figure: Figure 3. Component’s of AES[10].

3. INTELLIGENT TUTORING SYSTEMS (ITS)

A method of teaching and learning approach using ITScan be used to design learning. Approach to the systemis a paradigm change in teaching and learning. Accordingto Merril, PF, et al. and Shute, VJ and Psotka, J, a teachingsystem based on the concept of artificial intelligence hasbeen able to provide effective learning for students. [14][15].

Intelligent Tutoring Systems (ITS) are adaptiveinstructional systems applying artificial intelligence (AI)techniques. The goal of ITS is to provide the benefits ofone on-one instruction automatically. As in otherinstructional systems, ITS consist of componentsrepresenting the learning content, teaching andinstructional strategies as well as mechanisms tounderstand what the student does or does not know.Effectiveness learners with the ITS system is the abilityto understand the behavior of students. Due to, ITSconsists of components representing the learning content,teaching and instructional strategies as well asmechanisms to understand what the student does or doesnot know. Thus, an understanding of the ITS hasdeveloped into a system that is able to “understand” styleof learning, motivation, and provide flexibility in thepresent material. Ability is raised by ITS Franek, that canbe realized in its ability to deliver pedagogic materialcharacteristics according to students giving assignments,and assess capabilities [16].

ITS requires a dynamic model of learning, with a set ofrules or a related module, where this system has the abilityto evaluate some of the solutions with the right solutionsto respond to a user’s behavior. Next will be described amodel for ITS Elearning in figure 4, the model comprisesthe following modules: [17] Figure 4 Modules of ITS [17].

· Domain model: this provides the knowledge that thestudent will be taught, and consists of declarativeknowledge (lessons, tests, exams, etc.) and procedural

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knowledge (sets of rules to execute a task).· Student model: this records information about the

student (personal information, interaction and learningprocess parameters).

· Teacher model: this records information about theteacher (such as their personal information).

· Teaching model: this defines the students’ learningcycle. For this, it adjusts the presentation of the materialto each student’s knowledge according to theinformation contained in the student model. Theteaching model comprises seven modules: evaluationmodel, problem generation model, problemsolvingmodel, model for analyzing students’ answers, modelfor generating plan of revision units, model forpredicting students’ grades, and the syllabusgeneration model.

· Graphic interface: this is responsible for userinteraction with the intelligent tutoring system. 6

4 . ADAPTIVE HYPERMEDIA SYSTEM (AHS)Brusilovsky propose a model to AHS in two categories,namely: adaptive presentation and adaptive navigationsupport, as follows: [5] [18] Figure 5. AHS Model [5] [18].

4.1 Adaptive PresentationPresentation adaptive attempt to present information thatwill be introduced to a particular user through AHS, andadjust the content of a hypermedia page to the user. Thispage can make the selection of information that will beintroduced to the user. There are three methods ofpresentation adaptive system, namely: [5] [18].

· Adaptive Multimedia Presentation, is to provideadditional information, explanations, illustrations,examples, and others, for users who require the use ofan adaptive multimedia system.

· Adaptive Text Presentation, is to provide the adaptiveinformation system from a web page with the text typeis different and refers to the fact that jenisyang with thesame information can be introduced with a differentway different.

· Adaptive Of Modality, the model allows a user canchange the material information. For example, some userslike the only example of a defenisi information, whileother people who like the other information.

4.2 Adaptive Navigation SupportThe six-way Navigation Support adaptive methods,namely: [5] [18].

· Direct Guidance, direct visual guidance system. One ofthe nodes that have shown that this is a good node foraccess and recommend users to continue next web page.

· Adaptive Link Sorting, sorting the nodes of an adaptivesystem, so that all nodes on a particular web page thatwill be selected in accordance with the desire to modelusers.

· Adaptive Link Hiding, adaptive nodes that can hide aweb page in order to prevent users to access the webpage next to it because it is not relevant.

· Adaptive Link Annotation, additional notes are adaptivenode with a few comments to the web page.

· Adaptive Links Generation, nodes that can be adaptivemegenarate from the previous node for the user to link aparticular web page.

· Map Adaption, a map of the node structure adaptationof the system with a graphical way memvisualisainavigation system, to make a web page to the desireduser. 7

5. MOTIVATION STUDENT CHARACTERISTICSTo achieve the classification results of student motivationin learning can use the model Promoting Motivation Tutor(MPT). There are two components of the MPT, namely:motivation in the learning contents and motivation in thelearning exercise. [19].

Motivation in the learning contents have three variables:Time spent T(x) = {Fast, Medium, Slow}, The number ofactivities A(x) = {Many, Normal, Few}, and Help requestH(x) = {Yes or No} [9]. For example, if a student spent along time in the learning contents, conduct many activities,and ask for help . This motivation is represent as Rule CD5(Contents Diagnosis) = f(Slow, Many, Yes). Therefore,there are 18 (3*2*3) rules from the combinations ofelements in three variables in the learning contents (rulenumber CD1…CD18). For rules CD1…CD9 are student’smotivation and rules CD10…CD18 are student’s notmotivation. As shown in the table 1 below: Table 1.Motivation rules in the learning contents

RuleNumberTimespentThenumber ofactivities

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Helprequest MotivationCD1 Slow Many No YesCD2 Slow Normal No YesCD3 Medium Many No YesCD4 Medium Normal No YesCD5 Slow Many Yes YesCD6 Slow Normal Yes YesCD7 Medium Many Yes YesCD8 Medium Normal Yes YesCD9 Slow Few No YesCD10 Slow Few Yes NoCD11 Medium Few No NoCD12 Medium Few Yes NoCD13 Fast Many No NoCD14 Fast Normal No NoCD15 Fast Many Yes NoCD16 Fast Normal Yes NoCD17 Fast Few No NoCD18 Fast Few Yes NoIn the learning exercise can be divides the into fourvariables: Quality of Solving Problems Q (x) = {Hight,Medium, Low}, Time spent T (x) = {Fast, Medium, Slow},Hint or Solution Reques S(x) = {Yes or No}, and RelevantContents Request R(x) = {Yes or No} [9]. Therefore, thereare 36 (3*3*2*2) rule from the combinations of elementsin four variables in the learning contents (rule numberCD1…CD36). For rules CD1…CD18 are student’smotivation and rules CD19…CD38 are student’s notmotivation. As shown in the table 2 below:Table 2. Motivation rules in the learning exercises Basedon the results of the simulation table 1 and table 2 can beuse as the standard rules implementation in student’smotivation .86. ARCHITECTURE DESIGN AES MOTIVATIONThe development a model of adaptive e-learning systembase on student’s motivation in this study will be combinesome components of the AES, ITS, AHS and MPT[9,10,17,18,19]. A model AES Motivation are consists ofseveral components, namely: domain model, studentmodel, teacher model, pedagogic model, adaptation model,graphic interface, motivation in learning contents andmotivation In learning exercise. Architecture design amodel AES Motivation can be described fully, as follows:Figure 6 Architecture Design AES Motivation Studyresults that have been conducted by W. Fajardo Contreras,et. al, “An Intelligent System fortutoring a Virtual E-learning Center” [17] is the basis arsiektur of a modelAES Motivasion, above.

7. CONCLUSIONThe development of intelligent and adaptive Elearningsytem based on the characteristics of student’s motivationlevel can combine several components ITS, AHS, andMPT. ITS components are domain model, student model,teacher model, pendagogic model, and the graphicinterface.Support. AHS with the adaptive modules: presentationadaptive and adaptive navigation support, can supportAES adaptive motivation.While the MPT variables: learning in contens and learningin exercise, can support AES Motivation in determiningthe characteristics of student’s motivation.Architecture design of a model AES Motivation developedfrom several researches: AES, ITS, AHS, and MPT. Thesystem can accommodate the delivery of materials adaptivelearning, and knowing the characteristics of student’smotivation in learning process.

FUTURE RESEARCH

Research on the classification of student’s motivationhave been conducted, with Naive bayes method. Resultsof the use is already on trial in a small scale, Sfenrianto[20].

REFERENCES

[1] Hasibuan, Zainal A and Harry B. Santoso, (2005) “TheUse of E-Learning towards New LearningParadigm: Case Study Student Centered E-Learning Environment at Faculty of ComputerScience – University of Indonesia”, ICALT TEDC,Kaohsiung, Taiwan.

[2] Vicente, A. D. (2003) “Towards Tutoring Systems thatDetect Students’ Motivation: an

Investigation”, Ph.D. thesis, Institute for Communicatingand Collaborative Systems, 9 School of Informatics,University of Edinburgh, U.K.

[3] Yong S. K., Hyun J. C., You R. C., Tae B. Y., Jee-H. L.(2007) “A Perspective Projection Tutoring SystemWith Motivation Diagnosis And Planning,”,Proceedings of the ASME International DesignEngineering Technical Conferences & Computersand Information in Engineering Conference IDETC/CIE September 4-7, 2007, Las Vegas, Nevada, USA.

[4] Soldato, T. D., (1994) “Motivation in Tutoring Systems”,Ph.D. thesis, School of Cognitive and ComputingSciences, The University of Sussex, U.K. Availableas Technical Report CSRP 303.

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[5] Brusilovsky, (2001) “Adaptive Hypermedia. UserModeling and User-Adapted Interaction”, vol. 11,no. 1–2 p.p. 87–110, 2001.http://www2.sis.pitt.edu/~peterb/ papers

/brusilovsky-umuai-2001.pdf [Accessed May 18th, 2008].

[6] Nwana, H. and Coxhead, P. (1988), “Towards anIntelligent Tutor for a Complax MathematicalDomain”, Expert System, Vol. 5 No. 4.

[7] Stoyanov and Kirschner, (2004) “Expert ConceptMapping Method for Defining the Characteristicsof Adaptive E-Learning”, ALFANET Project Case.Educational Technology, Research &Developement, vol.52, no. 2 p.p. 41–56.

[8] Modritscher et al. (2004) “The Past, the Present andthe future of adaptive ELearning”.

In Proceedings of the International Conference InteractiveComputer Aided Learning (ICL2004).

[9] De Bra et al. (1999) “AHAM: A Dexter-based ReferenceModel for Adaptive Hypermedia”, In Proceedingsof the 10th ACM Conference on Hypertext andHypermedia (HT’99), P.p. 147–156.

[10] Brusilovsky and Maybury, (2002) “From adaptivehypermedia to the adaptive web”, Communicationsof the ACM, vol. 45, no. 5 p.p. 30–33.

[11] Hadj M’tir, et all, “E-Learning System Adapted ToLearner Propil”, RIADI-GDL Laboratory, NationalSchool of Computer Sciences, ENSI, Manouba,TUNISIA, http://medforist.ensias.ma/Contenus/Conference%20Tunisia%20IEBC%202005/papers/June24/08.pdf [Accessed: Jun 4th, 2009].

[12] Nora Koch, (2000) “Software Engineering forAdaptive Hypermedia Systems”, PhD thesis,Ludwig-Maximilians-University Munich/ Germany,2000. http://www.pst.informatik.uni-muenchen.de/personen/kochn/PhDThesis Nora Koch.pdf[Accessed: Jan 10th, 2008]

[13] Henze and Nejdl, (2003) “Logically CharacterizingAdaptive Educational Hypermedia Systems”, InProceedings of InternationalWorkshop onAdaptive Hypermedia and Adaptive Web-BasedSystems (AH’03), P.p. 15–29. AH2003.

[14] Merrill, D. C., P.F. et al, (1996) “Computer AssistedInstruction (CAI)”, Mac Millan Publishing.

[15] Shute, V.J. and Psotka, J., (1998) “Intelligent TutoringSystem: Past, Present and Future”, abstract forchapter on ITS for handbook AECT.

[16] Franek, (2004) “Web-Based Architecture of anIntelligent Tutoring System for Remote StudentsLearning to Program Java”.

[17] W. Fajardo Contreras1 et. al. (2006) “An IntelligentTutoring System for a Virtual Elearning Center”,Departament Computación Intelligent Artificial,E.T.S. Fakulty of Informática, University ofGranada, 18071 Granada, Spain.

[18] Brusilovsky, (1996) “Methods and Techniques ofAdaptive Hypermedia”, User Modeling and User-Adapted Interaction, vol. 6, no. 2–3 p.p. 87–129,http://www2.sis.pitt.edu/~peterb/ papers/UMUAI96.pdf [Access May 18th, 2008].

[19] Yong S. K. , Hyun J. C., You R. C., Tae B. Y., Jee-H. L.(2007) “An Intelligent Tutoring System withMotivation Diagnosis and Planninga”, CreativeDesign & Intelligent

Tutoring Systems (CREDITS) Research Center, School ofInformation & CommunicationEngineering,Sungkyunkwan University, [email protected].

[20] Sfenrianto, (2009) “Klasifikasi Motivasi KontenPembelajaran Dalam ITS Dengan Metode NaïveBayes”, research reports, independent task:Machine Learning.

Sfenrianto is Doctoral Program Student in ComputerScience Department University of Indonesia. Heis also lecture in Postgraduate Masters of ComputerScience Department, Faculty of Computer Science,STMIK Nusa Mandiri Jakarta. He received hismaster degree in Magister of InformationTechnology STTIBI Jakarta, Indonesia. He majoredin Computer Science/Information Technology andminored in ELearning Adaptive System (AES).

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Asep SaefullahComputer System DepartmentSTMIK Raharja – Tangerange

Mail: [email protected]

Sugeng SantosoInformation Engineering DepartmentSTMIK Raharja – Tangerange

Mail: [email protected]

ABSTRACTAlong with the microcontroller technology that very rapidly develops and in the end it brings to Robotics technologyera. Various sophisticated robot, home security systems, telecommunications, and computer systems have usemicrocontroller as the main controller unit. Robotics technology has also been reaching out to the entertainment andeducation for human. One type of robot that is the most attractive type is the wheeled Robot (Robotic Wheeled). WheeledRobot is the type of robot that using a wheel likes a car in its movement. Wheeled Robot has limited movement, its can onlymove forward and only have control on the speed system without any control. By combined the microcontroller systemand embedded technologies we can obtain the Smart Robotic Wheeled (SWR). To that we can design a system of SmartWheeled Robotic that capable of detecting obstacles, move forward, stop, rewind, and turn to the left to right automati-cally. It is possible to create by using microcontroller AT89S2051 on embedded system based technology combine withartificial intelligence technology. Principle work of SWR is that the infra red sensor senses an object and sends back theresults of the object senses to process by microcontroller. Output of this microcontroller will control the dc motor so theSWR can move according to the results of the senses and instruction from microcontroller. The result is a prototype SmartWheeled Robot (SWR) that has an ability to avoid obstacles. In the future, Smart Wheeled Robot (SWR) prototype can bedeveloped to be implemented on the vehicles so it can increase the comfort and security in the drive.

Keywords: Smart Wheeled Robotic, Microcontroller, Automatic Control

Saturday, August 8, 200915:10 - 15:30 Room AULA

SMART WHEELED ROBOT (SWR) USING MICROCONTROLLERAT8952051

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INTRODUCTIONAlong with the rapid development of microcontroller tech-nologies that ultimately deliver our technology in an era ofRobotics, has made the quality of human life is high. Vari-ous sophisticated robot, home security systems, telecom-munications, and many computer systems usemicrocontroller as the main controller unit. Robotics tech-nology development has been able to improve the qualityand quantity production of various factories. Roboticstechnology has also been reaching out to the entertain-ment and education for people. Car robot is one type ofrobot that is the most attractive type. Cars robot is a kindof robot that movement use the withdrawal wheel car, al-though it can use only two or three wheels only. The prob-

lems that often occur in the design and create a robot carsis the limited ability of a robot car that can only moveforward only, and only on the speed control system with-out the control car robot.By using microcontroller and embedded technology thatcan be designed artificial control car robot automatically,that is controlling the robot car that is capable of movingforward, stop, rewind, and turn automatically. Electionsembedded systems with embedded systems that can beeasily compared with

the multi-function computer, because it does have a spe-cial function. Embedded a system can be very good abilityand is an effective solution of the financial side. Embed-

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ded system consisting of a general one board with themicro-computer program in a ROM, that will start a spe-cific application is activated shortly after, and will not stopuntil disabled.One important component which is a tool from sensesSmart Wheeled Robotic (SWR) is a sensor. Sensor is toolthat can be detect something that is used to change thevariation mechanical, magnetic, heat, and chemical raysinto electric current and voltage, the sensor used in theSWR is the infra red sensor. Infra red sensor selectionbased on the function of a robot that is designed to sensean object and sends back the results of the object sensesto process by microcontroller.Microcontroller before use in the system SWR must firstfill program. The goal of IC charging is to program thataims to work in accordance with the draft that has beenset. Software used to write assembly language programlisting is M-Studio IDE for MCS-51. IC microcontroller ini-tially filled with a blank start the program. While for the ICthat contains the program had been another, the programis deleted first before automatically filled in with the newprogram. Process of charging is using ISP Flash Program-mer, from AT89S2051 microcontroller producers namelyATMEL company.

SESSION

Discussion of the block diagramControl car robot automatically designed to be able to moveforward, stop, rewind, turn right and turn left indepen-dently. Mobile robot used in the design using this type ofmovement Differential Steering type movement, which ismost commonly used. Kinematics used is quite simple rela-tive position where it can be determined with the differ-ence in speed with the left wheel right wheel, the designhas two degrees freedom. With the motor left and rightone direction will cause the robot run forwards or back-wards, and with the opposite direction, the robot will ro-tate the opposite direction or counter-clockwise direction.Smart Robotic Wheeled (SWR) is designed with two typesof gearbox as the motor driving the wheels left and right,infra red sensor which functions to avoid collision withobjects in the surrounding areas so that the robot canwalk better without the crash.Control system design software SWR was built using theassembly language programming, which is then convertedinto the form of a hex (hexadecimal). Program in the form ofa hex this is later entered into microcontroller. Data re-ceived by microcontroller digital data is processed fromAnalog to Digital Converter which is owned bymicrocontroller itself. This feature is one of the most im-portant features in the design SWR is because all of theperipherals to support this move based on the robot input

digital data issued by the microcontroller To move the pro-gram from the computer into microcontroller use two mod-ules that act as intermediaries for the program. The systemblock diagram as a whole is as follows:IRCircuits

MicrocontrollerAT89S2051Motor 2DriverMotor DCMotor 1Push button START

Power Supply

Figure 1. Control system block diagram SWR

Infra Red SensorInfra red sensor is a sensor that emits rays below 400 nmwavelength, infra red sensor using a photo transistor as arecipient and led as the infra red transmitter. It will raise thesignal emanated from the transmitter. When the signal beamon the object, then this signal bounce, and received by thereceiver. Signal received by the receiver are sent to a seriesof microcontroller for the next series to give the commandto the system used in accordance with the algorithm pro-gram microcontroller made, as shown in the picture below:

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Figure 2. The working principles of infra red sensor

(Widodo Budiharto S.SI, M.Kom, Sigit Firmansyah, (2005),Elektronika Digital Dan Mikroprosesor, Penerbit Andi,Yogyakarta. )

Infra Red TransmitterTransmitters form a series of infra red rays that emit a cer-tain frequency between 30 - 50 kHz. Photo transistor willbe active when exposed to light from the infra red led. Ledthe photo transistor and separated by a distance. Far dis-tance proximity affects the intensity of light received byphoto transistor. Led and when the photo transistor is notobstructed by objects, the photo transistor will be active,causing the logic output ‘1 ‘and the Led go out. When thephoto transistor and Led obstructed by objects, phototransistor will be switched off, causing the logic output ‘0‘and Led light.

Figure 3. The scheme range infra red transmitter

(Widodo Budiharto S.SI, M.Kom, Sigit Firmansyah, (2005),Elektronika Digital Dan Mikroprosesor, Penerbit Andi,Yogyakarta.)

Infra Red receiverCircuit receiver can capture infra red signal with a frequency30 - 50 kHz, after the credits received in the form of thesignal converted into DC voltage. When the infra red sig-nal which caught the rebound, it will be strong enough tomake the output to be low.

Figure 4. The Scheme infra red receiver(Widodo Budiharto S.SI, M.Kom, Sigit Firmansyah, (2005),Elektronika Digital Dan Mikroprosesor, Penerbit Andi,Yogyakarta.)

In general system block diagram sensor is described asfollows:

Figure 5. Block diagram the sensor system

MicrocontrollerMicrocontroller is a major component or can be referred toas the brain that functions as the movement of motor (Mo-tor Driver) and processing data generated by comparatoras a form of output from the sensor. Microcontroller seriesconsists of several components including: AT89S2051, a10 K© resistor sized, measuring a 220 © resistor, a capaci-tor electrolyte measuring 10 ¼f 35 Volt, 2 pieces LED (LightEmitting diode) 2 pieces measuring 30 pf capacitors, a crystalwith 11.0592 MHz frequency, and a switch that serves tostart the simulation run. Of a series microcontroller schemeis as follows:

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Figure 6. The scheme Microcontroller

(Berkarya dengan mikrokontoller AT 89S2051 Nino GuevaraRuwano.2006, Elek Media Komputindo.Jakarta)

Packaged MCU AT89S2051 shown in the figure 7. Packageonly has 20 feet and has several ports that can be used asinput and output ports, in addition to supporting otherport, the port 1.0 to 1.7 and 3.0 port to port 3.7. Users mustbe adjusted to the rules set by the manufacturermicrocontroller this. AT89S2051 difference between theprevious series with the AT89S51 is the only pin P1 and P3in the AT89S2051, so that the MCU can not access theexternal memory for the program, so the program shouldbe stored in PEROM inside it with a 2 Kbytes, while all thepins were found in both MCU, the same function.

Figure 7. AT89S2051 Pin layout

(Berkarya dengan mikrokontoller AT 89S2051 Nino GuevaraRuwano.2006, Elek Media Komputindo.Jakarta)

Description of the function of each pin is as follows:

1. Pin 20, Vcc = Supply voltage microcontroller2. Pin 10, Gnd = Ground3. Port 1.0 - 1.1 = An 8-bit input / output ports which 2-way(Bidirectional I / O ports). Port pins P1.2 to P1.7 providepull-ups internally. P1.0 and P1.1 also function as a posi-tive input (AIN0) and the negative input (AIN1) is respon-sible on a comparison of analog signals in the chip. 1.0output port load currents of 20 mA and can be used to setthe LED directly. If a program to access the port pin1, thenthis port can be used as an input port. When the port pin1.2 to 1.7 is used as input port and port-port is set to bePulled-low, and then the port-port can generate cash (IIL)because of the internal pull-ups before. Port 1 can alsoreceive the code / data in the flash memory when the pro-grammed conditions or when the verification process isdone.

4. Port 3 3.0 to 3.5 is a six input / output pins that canreceive the code / data in two directions (bidirectional I / Oport) that have facilities internal pull-ups. P3.6 is a hard-

ware that is used as input / output of the comparator onchip, but the pin can not be accessed as a port input /output standard. Port 3 pins can issue a flow of 20mA. Port3 also provides the function of special features that varyfrom microcontroller AT89S2051, among others:

Table 1. Fungsi Port 3(Berkarya dengan mikrokontoller AT 89S2051 Nino GuevaraRuwano.2006, Elek Media Komputindo.Jakarta)Port Pin Alternate Functions

Port 3 also receives some control signals for Flash memoryprogramming and verification of data.5. RST = RST foot to function as a reset input signal. Allinput / output (I / O) will be back in the position of zero(reset) as soon as possible when reset is logically high.RST pin for a two machine cycles while the oscillator isrunning will cause the reset system all devices that are in aposition to zero.6. As = XTAL1 input to the inverting amplifier and oscilla-tor to provide input to the internal clock operating circuit.7. XTAL2 = As output from the foot of a series of invertingamplifier oscillator.

Motor DriverDC drive motor required to rotate the motor is used as therotation of the wheels, to move motor, and used a series ofintegrated (Integrated Circuit) L293D. L293D is a mono-lithic IC that has a high voltage (for 36 volt), which hasfour channels and is designed to accept TTL-level voltageand the DTL for moving the inductive loads such as sole-noid, stepper motor and DC motor. For ease in applicationL293D has two bridges which each consist of two entriesthat are equipped with one enable line. For more detailscan be seen in the picture 3.4. L293D IC block diagram.

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Figure 8. IC L293D block diagram(Panduan Praktis teknik Antar Muka dan PemrogramanMicrocontroller AT89S51, Paulus Andi Nalwan, 2003, PTElek Media Komputindo, Jakarta)

Driver of DC Motor

How it works driver of a series DC motor is controlled onlythrough this port Microcontroller, as described above pin1 and 9 are as enable you to play and can stop the motorwhen flood with the electric current at 0 (to rotate) and 1(to stop), whereas for P0.0 and P0.1 are used to make play-ing the motor left and right while the P0.2 and P0.3 are usedto make playing the motor up and down, if P0.0 = 0 andP0.1 = 1 then rotate to the right M1 , and if P0.0 = 1 and P0.1= 0 and M1 rotates left, while for M2 will rotate upwards ifP0.2 = 0 and P0.3 = 1 and M2 will rotate to the top, and ifP0.2 = 1 and P0.3 = 0 and M2 rotate will down. Motor 1(M1) and Motor 2 (M2) mounted separately so that whenM2 rotate, it will rotate M2 as well because it is in thesection that has been driven by M2. And vice versa if theM1 rotates, the M2 will not participate because it is rotat-ing at a fixed position. Series of DC motor that is used canbe seen in the figure 9.

Figure 9. Circuit driving DC motor

Panduan Praktis teknik Antar Muka dan PemrogramanMicrocontroller AT89S51, Paulus Andi Nalwan, 2003, PTElek Media Komputindo, Jakarta

STARTPrepare RobotPress swichObstacle?

Straight movementObstacle?NoYesStop Robot

NoYesRobot turn leftStraight MovementStop a moment, Turn right

Figure 10. Life cycle flowchart program control SWR

Robot prepared, and then presses the button to turn onthe robot, robot starts moving forward. Than walk straightup to face a hurdle, robot will pause and then move to theright to avoid obstacles. If still have obstacles the robotwill move to the left to avoid the obstacles, the robot goesback straight.

The design prototype SWRIn the design to build a prototype SWR, designed as aseries of images on the following:

Figure 11. The prototype scheme SWR

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The Infrared (IR) sensor to detect obstacles, the data issent from IR to microcontroller. Microcontroller can be readfrom the IR and processed in accordance with the flow-chart of the program is designed. Exodus frommicrocontroller L293D provide a signal to a DC motor driv-ing. DC motor will be react (rotate) either clockwise orcounter-clockwise the opposite according to the signaloutput from microcontroller which is the response of theinfra red sensor.

Design Software The program initializationsProgram designed to control for the SWR using assemblylanguage, while microcontroller only able to access a digi-tal input signal logic high (logic “1”) and logic low (logic“0”) so that the need to perform initialization.

;======================================================; SWR.ASM;———————————————————————————; NAME : PROTOTYPE SWR;FACULTY : SISTEM KOMPUTER ( SK );SEKOLAH TINGGI MANAJEMEN DAN ILMUKOMPUTER RAHARJA;=====================================================;; INITIALISATION;——————————————————————————;SWITCH BIT P3.7 ; PIN 11SENS_KANAN BIT P1.0 ; PIN 12SENS_KIRI BIT P1.1 ; PIN 13IN_1 BIT P3.0 ; PIN 2IN_2 BIT P3.1 ; PIN 3IN_3 BIT P3.2 ; PIN 6IN_4 BIT P3.3 ; PIN 7LED_1 BIT P1.5 ; Right sensor indica-torLED_2 BIT P1.6 ; Left sensor indica-torPOW_IND BIT P1.7 ; Power indi-cator

;======================================================;SUBRUTIN MAIN PROGRAM;=============================================================================================================ORG 00H ;START ADDRESSSWITCH_ON:SETB SWITCHJB SWITCH,SWITCH_OFF ;CLR POW_IND ;

POWER ONACALL DELAY ; CALLSUBRUTIN

Writing Program Assembly ListingBefore microcontroller used in the SWR system, must firstfill program. Charging that the program is aimed IC canwork in accordance with the draft that has been set. Soft-ware used to write assembly language program listing isM-Studio IDE for MCS-51.

Figure 12. Display M-Studio IDE

After writing the program listing on the M-Studio IDE texteditor is finished, and the text is saved in a file with thename MOBIL.ASM. This must be done because the soft-ware only works on file with the name *. ASM or *. A51.The next step is to compile assembly files into hex files, sofile into the file MOBIL.ASM will MOBIL.HEX. By press-ing the F9 key on the keyboard or via the menu. Hex file *.This will be inserted into the IC microcontroller. After thestep - this step is done then there will be some of the filesafter the compilation step, namely: MOBIL.ASM,MOBIL.LST, MOBIL.DEV and MOBIL.HEX. and until thisstage in the process of writing and compiling the programis completed assembly.

Enter into the program MicrocontrollerAt this step, the IC microcontroller initially filled with emptystart the program. While for the IC that contains the pro-gram had been another, the program is deleted first beforeautomatically filled in with the new program. To begin,first open the program ISP Flash Programmer is created bythe microcontroller AT89S2051 producers is the ATMELCompany. Then select the device that will use theAT89S2051

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Figure 13. Device Used AT89S2051

After selecting a device that is used, the menu on the “Hexfile to load flash buffer.” Software and then ask for theinput file. Hex will entered into the IC microcontroller, inthis case is MOBIL.HEX. File. Hex will entry that has beenrecognized by the software are then entered into the ICmicrocontroller. Then select the menu and search instruc-tion Auto Program menu or press Ctrl + A on the boardkeyboard.Software for the command in the file. HEX to the ICMicrocontroller, after the auto program is selected, fol-lowed by pressing the enter key. Next appears the viewthat the charging process, the stages of this processmicrocontroller are charging that the software is done byM-Studio IDE to listing all the programs into the IC isMicrocontroller.

IC Microcontroller fully in line with the increase in per-centage that appear in the software process of each trans-fer. Charging process begins with the “erase Flash &EEPROM Memory”, which means the software to performdeletion of the internal memory IC Microcontroller beforeput into the program is IC. In the process of deleting this,when percentage has reached 100% then means that theinternal memory has been erased completely and in a stateof empty. If percentage has not reached 100% but the soft-ware shows an error sign, then the elimination process tofail. This failure is usually caused by an error in the hard-ware the downloader. After the removal of the finishedsoftware to automatically “Verify Flash Memory.” Thenthe software starts filling with IC Microcontroller file. Hex.As with the deletion, the process is shown with the addi-tion of percentage. 100% indicates that the IC

Microcontroller has been fully filled. And marks the emer-gence of error indicates the process failed, which is usu-ally caused by errors in the hardware the downloader. Af-ter the steps - the steps above to run and complete, thenthe IC Microcontroller in the design tool is the typeAT89S2051, already can be used to perform the work sys-tem design tool.

Test and MeasureOnce you’ve finished all the string parts that are used todesign the system automatic control car robot, the next isa series of trials conducted on the entire block of the seriesof car robot control system is automatic.

Test Blocks SensorTests on a series of blocks are done with a sensor voltagepower portion that is provided. In this test the goal to beachieved is a sensor can detect an object located in frontof the infra red sensor. Trial block sensor consists of aseries transmitter and receiver. The input voltage at theentrance of each series that is 9 VDC. Sensitivity distancethat can be located by sensors located in the table below:

Table 2. Trial Results Sensitivity Sensor

In the table above in mind that the most far distance thatcan be located by the sensor 25 cm. Meanwhile, over 25cm, sensor is not working.

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Figure 14. Circuits of sensor block

Test and Measure Blocks MicrocontrollerTests in this series use LED as an indicator and does nothave the data out of each - each bit is determined to con-trol DC motor driver series. While for the bit assigned toreceive input of a series of infrared sensor in this experi-ment using a switch that is connected with GND, so thatwhen the switch is pressed the tip connected to the bitthat is determined “0”. This condition will be the same asthe situation in which when applied with a series of infrared sensor to detect the object. For more information pinwhich is used to read and control the external device canbe seen in the table below.

Table 3. Microcontroller connection with the circuits ofsensor

Table 4. Microcontroller connection with the circuits DCMotor

Test and Measure Blocks Motor DriverMicrocontroller AT89S2051 is the brain of all systems ofthis mobile robot. Microcontroller receive logic functionof a series of infra red sensors and analyze the incomingdata and send the driver a series of logic in the motor, so itcan shut off the motor and DC.By set the microcontroller series of SWR on this car thenthe robot can work independently, able to take decisionsin accordance with the conditions (Autonomous Robotic).

Motor driver block test performed to determine whetherthe series of motor driver can control the motor well. Inthis test, the logic is to give input from the motor driver tosee the direction of motor cycles. Voltage logic of a givenvoltage and +5 Volts to run a given motor is +5 Volts. Mo-tor right connected to the output 1 and output 2 on thedriver, while the left motor is connected to the output 3and output 4. Results from the testing can be seen in thetable below.

Table 5. Motor Driver Test Results

CONCLUSION

Overall results of testing and analysis system Smart Ro-botic Wheeled (SWR), can be as follows:

1. Car speed control system can work automatically without the need to use the remote and in control through`microcontroller; SWR can avoid obstacles and movefreely to the left and right.

2. The distance is ideal for sensor sensitivity is 2 cm - 20cm, between 21 cm -25 cm sensor sensitivity has beenless good and more than 26 cm sensor is not able towork.

3. Type differential steering can be used for theprototype so that the SWR can turn to the right and

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www.datasheetcatalog.com (Downloaded on March4, 2007)

7. Fairchild. DM74LS04. http://www.datasheetcatalog.com(Downloaded on March 4, 2007) Motorola.SN74LS147. http://www.datasheetcatalog.com(Downloaded on March 4, 2007)

8. Untung Rahardja, Simulasi Kendali Kecepatan MobilSecara Otomatis, Journal CCIT, Vol.2 No. 2 – Januari2009

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Paper

Information Technology Department – Faculty of Computer Study STMIK RaharjaJl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

email: [email protected]

AbstractionInformation technology Exploiting (TI) by various organization in general aim to to facilitate and quicken executionprocess business, improving efficiency, quality and ability kompetitif. That way also with College of Raharja as organi-zation which is active in education. Through adjusment of technology information, various enforceable activity easierly,quickly, effective, efficient, and requirement of various type of information required by all level of management in Collegeof Raharja representing critical success factor (CSF) for organization can be fulfilled quickly, accurate and economize.One among product of information technology which have been created and used by College of the Raharja is RaharjaMultimedia Edutainment (RME). Technological this used to support and memperlancar of school activity execution, andfulfill requirement of information [of] which deal with [his/its]. Refering to that matter, this research aim to to knowfactors influencing accepted better or do not it him RME by his consumer. Also wish known [by] relation/link of [among/between] factors influencing acceptance RME. Model used to know acceptance of RME of at this research is model TAM(Technology Acceptance Model). Model of TAM in detail explain information technology acceptance (TI) with certaindimension which can influence technological acceptance by consumer. Model this place factor of attitude and everybehavior of consumer by using two especial variable that is benefit ( usefulness) and use amenity (easy of use). Antici-pated by acceptance of this RME is also influenced by other;dissimilar factor for example: Attitude Toward Using (ATU)Or attitude to use, Intention To Use (ITU) Or intention to use to produk/servis and Actual System Usage (ASU) Or usebehavior.

Keyword : RME, TAM, usefulness, easy of use.

Saturday, August 8, 200913:30 - 13:50 Room AULA

Study Perception of Technological Consumer of Study of Raharja MultimediaEdutainment ( RME) Use Method of Technology Acceptance Model.

Henderi, Maimunah, Aris Martono

1. AntecedentBesides used to facilitate execution process busi-

ness and improve ability kompetitif, exploiting andadjusment of technology information (TI) also can influ-ence speed, efficient and effectiveness of execution activ-ity of organizational business( inclusive of organizationmoving area of education execution). Others, TI have alsooffered a lot of opportunity to organization to increaseand mentransformasi service, market, process job, and thebusiness [relation/link]. In aspect of education manage-ment, applying of TI have influenced strategy and execu-tion process to learn to teach. strategy of Study of at thisera have been influenced by TI and instruct to way oflearning active siswa-mahasiswa coloured by problem-base-learning. Thereby, way of learning active guru-dosen[is] progressively left by [doing/conducting] enrichment

and use of information technology facility ( high impactlearningConception the study high impact learning, by CollegeRaharja applied createdly is tools study Raharja Multime-dia Edutainment (RME) supported by information tech-nology. Through applying TI with concept RME, result ofbelajar-mengajar expected by linear eksponensial/non,because RME integrate information technology and edu-cational. Others, RME also contain digital concept Inter-active of multimedia learning (IDML), library by Lecturer,continues improvement, and entertainment, borne anddeveloped collectively/together by Person Raharja. Tech-nological this claim domination of information technologyof media multimedia and for the student study activity.Technological use base on this multimedia is used in order

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to process student study can be done/conducted byinteraktif and support information domination and also thenew technology. Hence, applying RME of at process learnto teach [in] College Raharja of[is inclusive of strategyhigt impact learning with marking ( henderi, 2004): ( a) learnby interaktif, ( b) learn by just [is] in time learning, ( c) learnby hipernavigasi, ( d) learn by networking, ( e) learn bykolaboratif, and (f) learn by engaged [is] learning

Besides used as by tools study, RME also haveability in providing and fulfilling information requirementof which deal with process learn to teach quickly, precise,and economize representing one of Critical Success Fac-tor (CSF) for a College. Thereby, this technological useclaim domination of information technology of media mul-timedia and which is the inclusive of technology whichrelative newly to can reach target which have been speci-fied.Existence a new technology is area of informasi-komunikasiin the form of tools of this study, will yield reaction [of] [at]x’self of his/its consumer, that is in the form of acceptance( Acceptance) and also deduction ( Avoidence). But thatway, with [do] not barricade of a technology come into a[nbusiness process, hence it is important to know how ac-ceptance a the technology for [his/its] consumer.

2. Problems

Problem which wish dikemukan and discussed in by thisresearch is1. Any kind of factors which interact and have an effect

on to storey;level technological acceptance speciallyRaharja Multimedia Edutainment to all dosen and student in College Raharja

2. How form model acceptance of information technology that is Raharja Multimedia Edutainment appliedin College Raharja

3. Hypothesis Hypothesis of Public raised in this researchis Anticipated by a model raised at this research issupported by fact [in] field. This matter [is] indicationthat anticipation of matrix of varians-kovarians of population of is equal to matrix of varians-kovarians sampel(observation data) or can be expressed “p = “s.

3. Special hypothesis at this research is

1. Anticipated by Perception of Amenity use RaharjaMultimedia Edutainment ( Perceived Ease of Use/Peou)having an effect on to Benefit Perception ( PercievedUsefulness/Pu). Progressively easy to Raharja Multimount his/its benefit

2. Is Anticipated By Perception of Benefit of RaharjaMultimedia Edutainment ( Percieved Usefulness/Pu)having an effect on to Consumer Attitude ( AttitudeToward Using/Atu). Excelsior mount benefit of software AMOS hence positive progressively attitude ofconsumer in using the Raharja MultimediaEdutainment

3. Is anticipated by a Amenity Perception use RaharjaMultimedia Edutainment ( Perceived Ease of Use/Peou)having an effect on to Consumer Attitude (AttitudeToward Using/Atu). Progressively easy to RaharjaMultimedia Edutainment to be used hence positiveprogressively the consumer attitude in using theRaharja Multimedia Edutainment

4. Anticipated By a Consumer Raharja MultimediaEdutainment Attitude (Attitude Toward Using/Atu)having an effect on to Consumer Behavior ( Behavioral Intention to Use/Itu). Positive progressively theconsumer attitude in using Raharja MultimediaEdutainment hence progressively mount intention touse it

5. Anticipated By a Benefit Raharja MultimediaEdutainment Perception ( Percieved Usefulness/Pu)having an effect on to Consumer Behavior ( Behavioral Intention to Use/Itu). Excelsior mount benefitRaharja Multimedia Edutainment hence progressivelymount intention to use it.

6. Is Anticipated By a Consumer Raharja MultimediaEdutainment Behavior ( Behavioral Intention to UseItu) having an effect on to Real Usage ( Actual SystemUsage/Asu). Intention Excelsior to use softwareAMOS hence behavioral positive progressively inusing it.

4. Basis For Theory

a. Critical Success Factor ( CSF)Simply, Luftman J ( 1996) defining critical success factirs( CSF) is every thing (existing ) in organization whichmust be [done/conducted] successfully or succeedbetter. This definition hereinafter under considerationthis research [is] translated in conceptual context,where critical success factirs (CSF) represent factor ofkey of effectiveness of planning of adjusment of technology of informai by organization

b. Raharja Multimedia Edutainment ( RME).Raharja Multimedia Edutainment ( RME) is a tools ofstudy base on information technology containing concept of digital Interactive [of] multimedia learning (IDML), library by lecturer, continues improvement, and

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entertainment, recource sharing, borne and developedcollectively/together by Person Raharja ( Rahardja Benefit, Henderi, et all: 2007). Raharja MultimediaEdutainment represent strategy of technological implementation newly at activity of study in College Raharja.Technological this claim domination of information technology and media of multimedia for activity of studentstudy. Technological use base on this multimedia isused in order to process study of student can be [doneconducted] by interaktif and support domination ofinformation and also the new technology.

c. Technology Acceptance Model ( TAM)Model develop;builded to analyse and comprehendfactors influencing accepting of technological use ofcomputer, among other things which is registered inby various literature and reference of result of researching into area of information technology [is] Technology Acceptance Model ( TAM ).Model TAM [is] in fact adopted from model TRA (Theory Of Reasoned Action) that is theory of actionwhich have occasion to with one premis that reactionand perception of somebody to something matter, willdetermine attitude and behavior of the people (Ajzen,1975) of at ( DAVIS 1989). reaction And perception of consumer of TI will influence [his/its] attitudein acceptance of consumer TI, that is one of factorwhich can influence [is] perception of consumer usherbenefit and amenity of use of TI as an action whichhave occasion to in context of consumer of information technology so that the reason of somebody inseeing benefit and amenity of use of TI make action ofthe people can accept use TI.Model TAM developed from psychological theory, explaining prilaku of consumer of computer that is havebase to of at belief ( belief), attitude ( attitude), intensity ( intention), and the behavioral [relation/link] ofconsumer ( user behaviour relationship). Target modelthis to explain especial factors from behavior of consumer of TI to acceptance of consumer TI, in more theinch explain acceptance of TI with certain dimensionwhich can influence easily accepting of TI by the consumer ( user). Model this place factor of attitude fromevery behavior of consumer with two variable that is:(a) the use Amenity ( ease of use), ( b) Benefit ( usefulness). second of this Variable can explain aspect ofconsumer behavior ( Davis 1989) in Iqbaria et al, 1997).Its conclusion is model of TAM can explain that perception of consumer will determine [his/its] attitude inacceptance of use TI. Model this clearerly depict thatacceptance of use of TI influenced by benefit ( usefulness) and use amenity ( ease of use )

Mount acceptance of consumer of information technol-ogy determined by 6 konstruk that is: Variable from out-side the system ( external [of] variable), perception of Con-

sumer to amenity ( perceived ease of use), perception ofconsumer to usefulness ( perceived usefulness), consumerattitude ( attitude toward using), behaviour tendency (behavioral intention), and usage aktual ( actual usage) (DAVIS 1989).

Draw 1 Technology Acceptance Model (TAM) (DAVIS1989)

d. Perceived Ease of Use ( PEOU)Use Amenity Perception defined [by] as a(n) size measure[of] where somebody believe that computer earn [is] eas-ily comprehended. Some information technology use ame-nity indicator ( DAVIS 1989) covering a. Computer very iseasy learned b. Computer do easily what wanted by con-sumer c. Consumer skill can increase by using computer d.Computer very easy to be operated.

e. Perceived Usefulness ( PU)Perception of Benefit defined [by] as a(n) size

measure [of] where belief of somebody to use [of] some-thing will be able to improve labour capacity one who useit ( DAVIS 1989). Some dimension [of] about usefulnessTI, where the usefulness divided into two category, that is1) usefulness with estimation one factor, and 2) useful-ness with estimation two factor ( usefulness And effec-tiveness) ( Todd, 1995) [at] ( NASUTION 2004). Useful-ness with one factor cover a. Making easier work b. Usefulc. Adding productivity d. Heightening effectiveness e.Developing work performance

While usefulness with estimation two factor cover dimen-sion a. Usefulness cover dimension: making easier work,useful, adding productivity b. Effectiveness cover dimen-sion: heightening effectiveness, developing work perfor-mance

f. Attitude Toward Using ( ATU)Attitude Toward using the system weared in TAM de-fined [by] as a(n) storey;level of felt assessment ( negativ-ity or positive) experienced of by as impact [of] if/whensomebody use an technology in [his/its] work ( DAVIS1989).

Other;Dissimilar researcher express that attitudefactor ( attitude) as one of aspect influencing individualbehavior. attitude of Somebody consisted of [by] compo-

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nent kognisi ( cognitive), afeksi ( affective), and compo-nent [of] related to behavior ( behavioral components). (Thompson 1991) [at] ( NASUTION 2004 g. Intention ToUse ( THAT) Intention To Use [is] tendency of behaviourto know how strength of attention [of] a consumer to usea technology

Mount use a technology of computer [of] [at]one can diprediksi accurately from attitude of its attentionto the technology, for example keinginanan add peripheralsupporter, motivate to remain to use, and also the desire tomotivate other;dissimilar consumer ( DAVIS 1989). Re-searcher hereinafter express that attitude of attention touse [is] prediksi which is good to knowing Actual Usage (MALHOTRA 1999).

h. Actual System Usage ( ASU)Behavioral [of] real usage [is] first time concepted in theform of measurement of frequency and durasi of time touse a technology ( DAVIS 1989).Somebody will satisfy to use system [of] if they believethat the system [is] easy to used and will improve themproductivity, what mirror from behavioral condition [of]wearer reality ( Iqbaria 1997 5. Research Methodologies

5.1. Research TypeThis Research is inclusive of into type of researchExplaratory, that is containing research of verification ofhypothesizing develop;builded [by] through theory withapproach of Technology Acceptance Model ( TAM), testedto use software AMOS.

5.2. Sampel Research populationMethod used to get empirical data through kuesioner haveSemantic scale to of diferensial. With this method [is] ex-pected obtainable [of] rating consumer Raharja Multime-dia Edutainment acceptance [of] [at] College Raharja andminimize mistake in research.Consumer Raharja Multimedia Edutainment population ofat College Raharja is dosen student and in College Raharja.Sum up dosen student and which will be made by a re-sponder is as much 120 responder, where 60% is dosenand 40% again is student

5.3. Data Collecting MethodTo get data or fact having the character of theoretical whichdeal with this research is done/conducted by a bibliogra-phy research, by learning literature, research journal, sub-stance of existing other;dissimilar kuliah source and ofhis/its relation/link with problems which the writer study Besides through book research, data collecting is alsodone/conducted by using kuesioner. Kuesioner containquestion made to know how influence [of] [among/be-tween] variable of Perception of Amenity Use ( PerceivedEase of Use/Peou), Benefit Perception ( Perceived Useful-ness/Pu), Consumer Attitude ( Attitude Toward Using/

Atu), Behavioral [of] Consumer ( Behavioral Intention ToUse / THAT) and the Real Behavior ( Actual System Us-age/Asu) from responder to Raharja MultimediaEdutainment of at College Raharja

5.4. Research Instrument This Research use instrument kuesioner made by usingclosed questions. By using closed questions, responderearn easily reply kuesioner data and from that kuesionerearn is swiftly analysed statistically, and also the samestatement can be repeated easily. Kuesioner made by us-ing or Semantec Differential international scale

5.4.1. Konstruk Eksogenous ( Exogenous of Constructs)This Konstruk [is] known as by sources variables or inde-pendent of variable which do not diprediksi by other vari-able in model. [At] this research [is] konstruk eksogenouscover Perceived Ease of Use (PEOU) that is a[n level ofwhere somebody believe that a technology earn is easilyused.

5.4.2. Konstruk Endogen ( Endogenous Constructs)Is factors which diprediksi by one or some konstruk.Konstruk Endogen earn memprediksi one or some otherkonstruk endogen, but konstruk endogen can only corre-late kausal by konstruk [is] endogen. At this research [is]konstruk endogen cover Perceived Usefulness ( PU), Atti-tude Toward Using (ATU), Intention To Use (ITU) AndActual System Usage ( ASU). With amount kuesionerpropagated only as much 120 eksemplar and anticipatelow rate of return, hence this research use storey;levelsignifikansi, that is equal to 10% with assumption formengolah kuesioner with amount coming near minimumboundary [of] sampel which qualify.

5.4.3. Diagram conversion groove into equationAfter step 1 and 2 [done/conducted], researcher can startto convert the specification of the model into equationnetwork, among other things is:Structural Equation ( Structural Equations)This Equation [is] formulated to express causality [rela-tion/link] usher various konstuk, with forming variable lateneksogenous and endogenous measurement model, form[his/its] equation for example:

PU = ã11PEOU + ò1 (1)ATU = ã21PEOU + â21PU + ò2(2)ITU = â32ATU + â31PU + ò3 (3)ASU = â43ITU + ò4 (4)

Equation of is specification of measurement model (Mea-surement Model)Researcher determine [which/such] variable measuringwhich/such konstruk, and also with refer to matrix show-

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ing correlation hypothesized [by] usher konstruk or vari-able. Form equation of indicator of variable of lateneksogenous and indicator of variable of laten endogenousfor example :equation of Measurement of indicator of variableeksogenousX1 = ë11PEOU + ä1X2 = ë21PEOU + ä2X3 = ë31PEOU + ä3X4 = ë41PEOU + ä4X5 = ë51PEOU + ä5equation of Measurement of indicator of variable endog-enous.y1 = ë11PU + å1y2 = ë21PU + å2y3 = ë31PU + å3y4 = ë41PU + å4y5 = ë51PU + å5y6 = ë62ATU + å6y7 = ë72ATU + å7y8 = ë82ATU + å8y9 = ë93ITU + å9y10 = ë103ITU + å10y11 = ë113ITU+ å11y12 = ë124ASU+ å12y13 = ë134ASU+ å13y14 = ë144ASU+ å14Where this variable eksogenous variable endogenous andsecond [is] [his/its] clarification [is] visible [at] tables of 1.Research Variable which the Observation hereunder

Tables 1Research Variable which Observation

5.4.4. Examination Model To Base on Theory Examination model to base on theory done/conductedby using software AMOS of Version 17.0. In the follow-

ing is result examination of the model

Draw 2 Result Model Early Research

Hypothesis explaining empirical data condition by model/teori [is] : H0 : Data Empirik identik with theory or model ( Hypoth-esis accepted by if P e” 0.05). H1 : Data Empirik differ from theory or model ( Hypothesisrefused by if P < 0.05 )

Pursuant to Picture 2 showed [by] that theory model raised[at] this research disagree with population model whichobservation, because known that [by] value probability (P) [do] not fulfill conditions [of] because result nya below/under value recommended [by] that is > 0.05 ( GHOZALI2005).

Inferential temporarily that output model not yet fulfilledacceptance Ho conditions, so that cannot be [done/con-ducted] [by] a hypothesis test hereinafter. But that way, inorder to the model raised to be expressed [by] fit, hencecan be [done/conducted] [by] a modification model match-ing with suggested by AMOS

This research use Model Developmental Strategy, this strat-egy enable [doing/conducting] of modification model ifmodel raised [by] not yet fulfilled recommended condi-tions. Modification [done/conducted] to get model whichfit ( sesuai) with examination conditions ( WIDODO 2006).

Pursuant to theoretical justifikasi [is] which there have,[is] hence [done/conducted] [by] a modification model withstructural model change assumption have to be based onwith strong theory ( GHOZALI 2005

Pursuant to result Estimate and Regression Wieght, ishence [done/conducted] by a modification vanishedly isindicator variable which is non representing validkonstruktor for a[n variable laten of at structural modelraised. If value stimate [of] [at] loading factor (?) from anindicator variable < 0.5 hence the the indicator shall indrop ( GHOZALI 2004). Hereinafter to see signifikansi (Sig), value which qualify [is] < 0.05. If value Sig > 0.05

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hence can be said that by a the indicator non representingvalid konstruktor for a[n variebel laten and this matter bet-ter [in] drop ( dihapus) ( WIDODO 2006). Modification[done/conducted] as a mean to get value Probability >0.05 so that model expressed [by] fit ( according to). Atthis research is modification [done/conducted] in threephase.First step to do/conduct modification to modeldevelop;builded [by] [is] vanishedly is X3 ( amenity to belearned) and X5 (amenity to be comprehended) represent-ing valid indicator for measurement PEOU ( Perceived Easeof Use). Abolition [done/conducted] by because loadingfactor for the indicator which [his/its] value lower that isbelow/under 0.50 released from model.

Step second to [do/conduct] modification to modeldevelop;builded by is vanishedly is Y5 (costing effective)representing valid indicator for measurement PU ( PerceivedUsefulness). Abolition [done/conducted] [by] becauseloading factor for the indicator which his/its value lowerthat is below/under 0.50 [released] from model.

Third step to [do/conduct] modification to modeldevelop;builded [by] [is] vanishedly is Y14 ( customer/client satisfaction) representing valid indicator for mea-surement ASU ( Actual System Usage). Abolition [done/conducted] [by] because loading factor for the indicatorwhich its value lower that is below/under 0.50 [released]from model.

Tables 2 Modification Step

After [done/conducted] [by] a modification model, [is]hence got [by] a model which fit such as those which as

described [at] Picture 3

Draw 3 Final Model Examination Result of Research

5.4.5. Test of According to ModelCriterion Fit or [do] not it[him] the model do not is onlyseen from its value probability but also concerningother;dissimilar criterion covering Absolute size measure[of] Fit Measures, Incremental Fit Measures and Parsimo-nious Fit Measaures. To compare value which got at thismodel with critical value boundary [at] each the measure-ment criterion, visible hence at Tables in the following is

Tables 3 Comparison Test of According to Model

( Source : Process data of AMOS 17.0 as according toboundary of critical value ( WIDODO 2006)

Pursuant to above tables, hence can be told as a wholemodel expressed by fit ( according to). model raised atthis research is supported by fact [in] field. This matter isindication that matrix varians-kovarians populationanticipation [of] [is] equal to matrix varians-kovarianssampel ( observation data) or can be expressed “p = “s.At this research is done/conducted by a analysis modeltwo phase that is analyse CFA ( Confirmatory FactorAnalysis) and hereinafter analyse full model. the Analysissecond is indication that model expressed [by] fit ( sesuai)[of] good to each variable laten and also to model as awhole

6. Result of Examination6.1. Test of Parameter Model Measurement of VariableLaten

This Examination go together examination of validity andreliabilitas 1. Validity Examination Examination to validity of variable of laten done/conducted seenly assess Signifikansi ( Sig) obtained byevery variable of indicator is later;then compared to byvalue Ü ( 0.05). If Sig d” 0.05 hence Refuse H0, [his/its]meaning [is] variable of the indicator represent validkonstruktor for variable of certain laten ( WIDODO 2006)

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A. Variabel Laten Eksogen1. PEOU (Perceived Ease of Use)

Tables 3 Test of Parameter of Variable PEOU

B. Variabel Laten Endogen1. PU (Perceived Usefulness)

Tabel 4 Test of Parameter of Variable PU

2. ATU (Attitude Toward Using)

Tabel 5 Test of Parameter of Variable ATU

3. ITU (Intention to Use)

Tabel 6 Test of Parameter of Variable ITU

4. ASU (Actual System Usage)

Tabel 7 Test of Parameter of Variable ASU

2. Examination Reliabilitas 1. Examination Directly

This Examination [is] visible directly from output AMOSseenly [is] R2 ( Squared Multiple Correlation). Reliabilitasfrom a[n visible indicator maintainedly assess R2. R2explain to hit how big proportion of varians of indicatorexplained by variable laten ( while the rest explained bymeasurement error) by Ghozali ( 2005), ( WIBOWO 2006result of Output AMOS hit value R2 ( Squared MultipleCorrelation) shall be as follows.

Tabel 8 Squared Multiple Correlation for variable X(Eksogen)

Tabel 9 Squared Multiple Correlation for variable Y(Endogen)

Pursuant to visible above Tables that indicator X12variable own highest value R2 that is equal to 0.780inferential so that that variable laten PEOU havecontribution [to] to varians X12 [of] equal to 78 % whilethe rest 22 % explained by measurement error.Indicator Y16 variable represent indicator which at leastrealibel from THAT variable laten, because value R2 whichowning of [is] compared to by smallest of other indicatorvariable. result Output [of] above yielding test reliabilitasindividually.

2. Indirect Examination

Done/conducted]ly test reliabilitas merger, approachsuggested by is look for value of besaran CompositeReliability and Variance Extracted from each variable oflaten usedly is information [of] [at] loading factor andmeasurement errorComposite Reliability express size measure of internalconsistency from indicator a konstruk showing degreeuntil where each that indicator [is] indication a [common/public] konstruk/laten. While Variance Extracted show theindicator have deputized well developed konstruk laten (GHOZALI 2005) and ( FERDINAND).

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Composite Reability obtained with the following formula:( “ std. loading )2

Constuct – Reability = (“std.loading)2

+ “å j

Variance extracted obtainable throughformula hereunder:

“ std. loading 2

Variance – extracted =

“ std. loading 2 + “ å j

å j adalah measurement error å j = 1– (Std. Loading)2

Tables 10 Test of Reliabilitas Merger

At above Tables seen by that PEOU, PU, ATU and THATown value Composite Reliability of above 0.70. While ASUassess its Composite Reliability still below/under 0.70 butadmiting of told [by] realibel [of] because still be at rangevalue which diperbolehkan. critical Value boundaryrecommended for Composite Reliability is 0.70. But thethe number is not a size measure “ dead”. Its meaning, if/when research [done/conducted] have the character ofeksploratori, hence assess below/under the criticalboundary ( 0.70) even also admit of accepted (FERDINAND 2002). Nunally And Berstein ( 1994) in (WIDODO 2006) giving guidance that in researcheksploratori, assess reliabilitas [of] among 0.5 - 0.6assessed [by] have answered the demand formenjustifikasi a research result. variable Laten PEOU, PU,ATU, THAT and ASU mememuhi boundary assessVariance Extracted that is e” 0.50.. Thereby can be saidthat by each variable own good realibilitas

3. Hypothesis Examination Examination of this Hypothesis to know influenceusher variable of laten-external system-seperti of attables 11 Result of Examination of Hypothesishereunder

Tables 11 Result of Hypothesis Examination

Pursuant to above Tables, explainable that 1. Variable ofPerceived Ease of Use ( PEOU) have an effect on to variableof Perceived Usefulness ( PU 2. Variable of PerceivedUsefulness ( PU) have an effect on to variable of AttitudeToward Using ( ATU 3. Variable of Perceived Ease of Use( PEOU) have an effect on to Attitude Toward Using (ATU 4. Variable of Attitude Toward Using ( ATU) have aneffect on to variable of Intention to Use ( THAT 5. Variableof Perceived Usefulness ( PU) have an effect on to variableof Intention to Use ( THAT 6. Variable of Intention to Use( ITU) have an effect on to variable of Actual SystemUsage(Asu). Pursuant to test of above hypothesis, explainable hencethat use of software RME influenced by 5 variable of latenthat is Perceived Ease of Use ( PEOU), PerceivedUsefulness ( PU) , Actual System Usage ( ASU), IntentionTo Use ( ITU) And Attitude Toward Using ( ATU)

6.2. Interpretation Model Pursuant to modification model and result of hypothesisexamination, explainable hence that model got at thisresearch shall be as follows :

Draw 4 Research Model

155

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Pursuant to model of at picture 4 got [by] that model [of][at] this research [is] model TAM ( TechnologyAcceptance Model) by Davis ( 1989) . Variable influencinguse software RME of at this research cover PU ( PerceivedUsefulness), PEOU ( Perceived Easy of Use), AttitudeToward Using ( ATU), Intention To Use ( ITU) And ASU (Actual System Usage ). Amenity Variable ( PEOU) Use software RME have aneffect on to [his/its] benefit variable ( PU), as according to([ DAVIS 1989], 320). [His/Its] meaning progressively easyto software RME to be used hence progressively mountthe benefit software can be said that [by] primary factor[of] software RME accepted better by [his/its] consumer[is] because easy software to be used Amenity variable ( PEOU) Use software RME have aneffect on to Attitude Toward Using ( ATU). its[his] Easyuse software RME generate positive attitude to use [it] Benefit variable ( PU) have an effect on to Attitude TowardUsing ( ATU) [of] where after consumer know [his/its]benefit hence will generate positive attitude to use [it]

Benefit variable ( PU) have an effect on to Attitude TowardUsing ( ATU) [of] where after consumer know [his/its]benefit hence will generate positive attitude to use [it]. Benefit variable ( PU) have an effect on to Variable ofIntention to Use ( ITU) [of] where after consumer know[his/its] benefit hence will arise intention to use [it].Variable of Attitude Toward Using ( ATU) have an effecton to Intention to Use ( ITU) [of] where attitude whichpotif to use software RME generate intention to use [it]. Variable of Intention to Use ( ITU) have an effect on toASU ( Actual System Usage ) where intention to usesoftware RME generate behavior of consumer to use [it]. From model [of] exist in picture 4 seen [by] that Variableinfluencing use of software RME [of] [at] this researchcover PU ( Perceived Usefulness), PEOU ( Perceived Easyof Use), Attitude Toward Using ( ATU), Intention To Use( ITU) And ASU ( Actual System UsageAccording to Ajzen ( 1988), a lot of behaviors [done/conducted] by human being in everyday life done/conducted below/under willingness control (volitionalcontrol) perpetrator. Doing/Conducting behavior of below/under willingness control ( volitional control) is do/conduct behavioral activity for [his/its] own willingness.Behaviors [of] below/under this willingness control [is]referred as behaviorally [is] volitional (volitionalbehaviour) what [is] defined [by] as behaviors individuallywish it or refuse do not use it if they set mind onmelawannya. Behavioral of volitional (volitionalbehaviour) referred [as] also with behavioral term [of] thedesired ( willfull behaviours).Fight against from behavior for willingness by xself(volitional behaviour) [is] behavior obliged ( mandatory

include] data (it) is true the obligation or demand fromjob.following final model :

Draw 5 Final Model [of] Research.

Final model [of] this research [is] diuji-ulang bysoftware [is] AMOS to know storey;level of validityand reliabilitas [of] each;every third indicator [of]variable and also test hypothesis to know storey;levelof influence [of] [among/between] variable of eksogento second of variable of endogen and influence ushersecond of variable of endogen [of] like [at] some tableshereunder

6.3. Uji Validitas Model AkhirA. Variabel Laten Eksogen

PEOU (Perceived Ease of Use)

Tabel 12 Test Parameter of Variable PEOU

B. Variabel Laten Endogen 1. PU (Perceived Usefulness)

Tabel 13 Test Parameter of Variable PEOU

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3. ASU (Actual System Usage)

Tabel 14 Test Parameter of Variable PEOU

1.3.1. Uji ReliabilitasPengujian Secara Langsung

Result of value R2 (Squared Multiple Correlation) is likeat tables 15 and tables of 16 hereunder.

Tabel 15 Squared Multiple Correlation for variable X(Eksogen)

Tabel 16 Squared Multiple Correlation for variable Y(Endogen)

Where indicator Y13 variable own highest value R2 thatis equal to 0.851 inferential so that that variable laten ASUhave contribution [to] to varians [of] equal to 85 % whilethe rest 15 % explained by measurement error. Indicator Y4 variable represent indicator which at leastreliable from variable laten PU, because value R2 whichowning of [is] compared to [by] smallest [of] otherindicator variable. result Output [of] above yielding testreliabilitas individually

Tables 17 Result of Hypothesis Examination

Pursuant to model [of] [at] picture 5 got [by] that finalmodel [at] this research is modification from model TAM( Technology Acceptance Model) by Davis ( 1989).Variable influencing use software RME [of] [at] thisresearch cover PU ( Perceived Usefulness), PEOU (Perceived Easy of Use) and ASU ( Actual System Usage ). Amenity Variable ( PEOU) Use software RME have aneffect on to [his/its] benefit variable ( PU), as according to( DAVIS 1989). [His/Its] meaning progressively easy tosoftware RME to be used hence progressively mount thebenefit software can be said that [by] primary factor [of]software RME accepted better by [his/its] consumer [is]because easy software to be used Amenity variable ( PEOU) Use software RME have aneffect on to ASU ( Actual System Usage ). its[his] Easyuse software RME generate consumer behavior to use[it]. Benefit variable ( PU) have an effect on to ASU ( ActualSystem Usage). Consumer RME after consumer know [his/its] benefit hence will generate consumer behavior to useit.

7. ConclusionPursuant to examination [done/conducted] to hypothesis,inferential hence the followings 1. Model research of at]thisresearch is mandatory of its meaning is model made haveto be weared by consumer or obliged to [by] becomeattitude and intention to use [is] not paid attention to 2.Final model obtained [at] this research [is] modificationfrom model TAM ( Technology Acceptance Model) by [DAVIS 1989 3. Variable influencing use of software RME[of] [at] this research cover PU ( Perceived Usefulness),PEOU ( Perceived Easy of Use) and Actual System Usage( ASU 4. Variable of Perceived Ease of Use ( PEOU) havean effect on to variable of Perceived Usefulness ( PU 5.Variable of Perceived Usefulness ( PU) have an effect onto variable of Actual System Usage ( ASU 6. Variable ofPerceived Ease of Use ( PEOU) have an effect on to variableof Actual System Usage ( ASU).

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8. SuggestionAs for suggestion raised [by] as according to researchwhich have been [done/conducted] by is1. use Software RME have to be supported fully bymanagement party and given by a supporter facility forcertain matakuliah, for example existence softwarewindows media player to look on video2. Use Software RME from its system facet have to bedeveloped again for its benefit for example for the studentabsence so that dosen by using software RME can watchstudent attendance3. Moderating Factor for the basic structure of user TAM/ the factor of interest consisted of by gender, age,experience, intelectual capacity and type of techonolgy.At this research is moderating factornya do not too paidattention to and expected at elite hereinafter themoderating factor have to be paid attention to betterbecause paid attention toly [is] moderating factor resultnya will be more be good and the model yielded good also4. Indicator User interface ( dependent variable) at TAMconsisted of [by] attitude ( affect, cognition),behavioural intention and actual usage. [At] thisresearch [is] mengaju [of] [at] 5 variable that is PU (Perceived Usefulness), PEOU ( Perceived Easy of Use),Attitude Toward Using ( ATU), Intention To Use ( ITU)And ASU ( Actual System Usage ). Expected [at]research hereinafter mengaju to 3 the elementarykompenen5. Factor Contributing user acceptance (independent variable) [at] TAM consisted of [by]usefulness ( perceived), easy of use ( perceived),playfulness, subjectiveness, and facilitatingconditions. [At] this research is Factor Contributinguser acceptancenya [do] not too paid attention toand expected [at] elite hereinafter factor contributinguser acceptance have to be paid attention to betterbecause paid attention toly [is] factor contributinguser acceptance result nya will be more be goodand the model yielded good also

6. The Basic structure of uses technologyacceptance from TAM formed of [by] divisiblemoderating factor become two variable that isindependent variable and dependent variable. [At]research [is] hereinafter expected [by] two thevariable paid attention to better 7. In system havingthe character of mandatory, intention and attitudeproblem needn’t be paid attention to [by] because(it) is true distinguish from nature of this mandatory

is forced or obliged. In research hereinafter if usingmodel mandatory hence the intention and attitudeneedn’t be paid attention to

Bibliography

1. Davis F. D.(1989), Perceived Usefulness,Perceived ease of use of Information Technology,Management Information System Quarterly.

2. Fahmi Natigor Nasution (2004), “TeknologiInformasi Berdasarkan Apek Perilaku(Behavior Ascpect)”, USU Digital Library.

3. Ghozali, Imam A. (2005), Model PersamaanStruktural– konsep dan aplikasidengan program AMOS Ver. 5.0., BadanPenerbit Universitas Diponegoro, Semarang.

4. Henderi, (2004), Internet: Sarana StrategisBelajar Berdampak Tinggi, Jurnal Cyber Raharja,Edisi 1 Tahun I (Hal. 6-9), Perguruan TinggiRaharja

5. Iqbaria, Zinatelli (1997), Personal ComputingAcceptance Factors in Small Firm : A StructuralEquation Modelling, Management InformationSystem Quarterly.

6. Jogiyanto (2007), “Sistem InformasiKeprilakuan” ,Andi, Yogyakarta.

7. Luftman J (1996), Competing in TheInformatioan Age – Strategic Aligment inPractise, ed. By J. Luftman. Oxfort UniversityPress

8. Untung Rahardja, Henderi, Rosdiana (2007),Raharja Multimedia Edutainment MenunjangProses Belajar di Perguruan Tinggi Raharja,Cyber Raharja, Edisi 7 Tahun IV (Hal. 95-104)Perguruan Tinggi Raharja

9. Widodo, Prabowo, P.(2006), Statistika : AnalisisMultivariat. Seri Metode Kuantitatif. UniversitasBudi Luhur, Jakarta.

10. Yogesh Malhotra & Dennis F. Galetta (1999),“Extending The Technology Acceptance Modelto Account for Social Influence”.

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Paper

ABSTRACTEducation requires Information Technology (IT) for facilitating data processing, fastening data collecting, and provid-ing solution on publication. It is to reach the Education National Standard which needs a research. The survey will usequestionnaires as data collector for explanatory or confirmatory in constructing the System Development Life Cycle(SDLC) using structured and prototyping techniques, so a planning is by anticipating the changes. It comprises 5subsystems; Human Resources, Infrastructures & Equipment, Library, GIS Base School Mapping, and Incoming StudentsEnrollment. The construction is aimed to ease information access connected among schools, Education Service, andsociety, integrating and completing each other.

.Key Words: System, Information, Education, and Wireless

Saturday, August 8, 200916:00 - 16:20 Room L-211

WIRELESS-BASED EDUCATION INFORMATION SYSTEM INMATARAM: DESIGN AND IMPLEMENTATION

1. INTRODUCTIONBackground

National education functions to carry out capa-bility and to build the people grade and character in theframe work of developing the nation, aims to improve stu-dent potentials for being faithful and pious human to TheGreat Unity God, having good moral, healthy, erudite, in-telligent, creative, autonomous, and being democratic andcredible citizen (Regulation of National Education System,2003).

Education is also a key to improve knowledgeand quality of capability to reach upcoming opportunityto take part in the world transformation and future devel-opment. How significant the education role is, so oftenstated as the supporting factor for economic and socialdevelopment of the people (Semiawan, 1999). This impor-tant role has placed the education as the people need, sothe participation in developing the education is very im-portant (Tajuddin,M, 2005).

The improvement of education relevancy andquality in accordance with the needs of sustainable devel-

opment is one of formulations in the National Work Meet-ing (Rakernas) of National Education Department(Depdiknas) in 2000. Result from the meeting is efforts toimprove education quality in order that student possessesexpertise and skill required by the job market after gradu-ated (Hadihardaja, J, 1999).

All the efforts could not be immediately imple-mented but should follow phases in education fields sub-ject to the regulations e.g. learning process to improvegraduates quality, increment of foreign language profi-ciency especially English, and IT usage (Tajuddin M, 2004).It is known that education and knowledge are importantcapitals to develop the nation. Their impact on the achieveddevelopment is unquestionable. Appearance of technol-ogy innovation results could change the way of life andviewpoint in taking life needs requirement of a nation. So itis concluded and proved that education provides stronginfluences to create changes on a nation(Wirakartakusumah, 1998).

159

Muhammad TajuddinSTMIK Bumigora Mataram West Nusa Tenggara, e-mail: [email protected]

Zainal HasibuanIndonesia University, e-mail: [email protected];

Abdul MananPDE Office of Mataram City, email: mananmti@gmail,com

Nenet Natasudian JayaABA Bumigora Mataram, e-mail: [email protected]

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The education system is in accordance with thenational education system standard. Every educationalinstitution must have its own condition, scope, and wayof managing the education process. However, there is stillsimilarity in management standard so quality provided byeach educational institution is according to the conductedassessment standard (Slamet, 1999).

In the Government Rule (PP) No. 19, 2005, forEducation National Standard (SNP) in Chapter II Sectionof Education National Standard, comprises:a. Standard of content;b. Standard of process;c. Standard of graduate competency;d. Standard of educator and educational staff;e. Standard of infrastructure & equipment;f. Standard of management;g. Standard of funding; andh. Standard of evaluation.

For the eight education standards, it seems im-portant to provide an information technology system tofacilitate the information of infrastructure, education staff,library, etc., either on education unit level or on the gov-ernment office level (Education Service), (Tajuddin M,2007).

The education system sometimes integrated manyrelated parts to combine its service performances. The in-volved parts are equipment, education staffs, curriculum,and so on. The mentioned parts are sectors taken in gen-eral from the common education systems. Integrated edu-cation system always fuses many parts to manage the pro-cess (Tajuddin M, 2006).

An education institution needs an integratedsystem which creating service system should be benefit-ing the schools, the Diknas, Local Government, and com-munities in occasion of transparency and accountability.Thus, the integrated education system has two main char-acteristics; the first is management process of internal sys-tem, and the second, is external service process. The inter-nal management process aims to meet an effective systemmanagement process. Occurred here is an inter-sectionusing data network structure existed in the educationalenvironment (Miarso, 1999).

Each section is able to know the newest data ofother sections that facilitating and more fastening the pro-cess of the other sections. Meanwhile, the external man-agement process has more direction to the services forcommunities. This process aims to provide easy and rapid

services on things connected with equipment and infra-structures of education. Integrated information system ofeducation equipment and infrastructures offers many ben-efits such as:

1. Facilitating and modifying the system. An integratedsystem commonly comprises modules that separatedeach other. When system change occurred, adjustmentof the application will easily and quickly perform. Asthe system change could just be handled by adding orreducing the module on application system.

2. Facilitating to make on-line integrated system. It meansthat the system could be accessed from any place,though it is out of the system environment. This online ability is easy to make because it is supported bythe system integrated with the centered data.

3. Facilitating the system management on the executivelevel. Integrated system enables executive board toobtain overall view of the system. So the control process could be easier and entirely executed (Leman, 1998).

Besides having those strengths, the integrated system hasalso weaknesses. Since the saved data is managed cen-trally, when data damage occurred, it will disturb the en-tirely processes. Therefore, to manage the database needsan administrator whose main function is to maintain andduplicate data on the system (Tajuddin M, 2005). Also, anadministrator has another function to be a regulator ofrights/license and security of data in an education envi-ronment, and so is in Mataram.

Department that responsible in education man-agement in Mataram is the Diknas (National Education),supervises three Branch Head-offices of Education Ser-vice [Kepala Cabang Dinas (KCD)] those are KCD ofMataram, KCD of Ampenan, and KCD of Cakranegara.Mataram has 138 state elementary schools (SDN) with40.604 students, 6 private elementary schools having 1.342students. Meanwhile the state Junior school (SMPN) ithas 21 schools with 15.429 students, and private juniorschool amounts to 8 schools having 1.099 students. It has8 state high schools (SMAN) having 5.121 students, 16schools of private having 9.623 students, state vocationalhigh school (SMKN) amounts to 7 schools having 4.123students and the privates have 6 schools with 1.568 stu-dents (Profil Pendidikan, 2006).

In 2006, granted by the Decentralized Basic Edu-cation Project (DBEP), Mataram Education Service haswireless network connected with three sub-district BranchOffices of Service, those are Mataram, Ampenan, and

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Cakranegara, which will be continued in 2007 by connect-ing sub-district for Junior and High schools, and clusterfor elementary schools. Three constructions of wirelessnetwork will be built in 2007 for high school sub-rayon, 6constructions for junior school sub-rayon, and 15 con-structions for elementary school cluster; the total is 24wireless network connections for schools and three forthe KCDs. (RPPK, 2007). To know the wireless networkconstructed in supporting the education system ofMataram, it needs development of an education informa-tion system that integrated each other in a system namedEducation Information System of Mataram. So the dataand information access, either for schools, Diknas, andcommunity could be executed swiftly in occasion of im-proving education services in Mataram based on the in-formation technology or wireless network.

B. PROBLEM FORMULATION

From the above background, problems could beformulated as follow: “Is the Design Construction ofSchool Base Education Information Using Wireless Net-work in Mataram able to process the education informa-tion system rapidly, properly, and accurately in improvingthe educational services?”

C. PURPOSE OF RESEARCH

1. Procuring information system application integratedbetween one subsystem with others which will provide the following data on educational units:

· Data of number of teacher and their functional position on respective educational unit, and number ofelementary school class teacher, and number of teacherper class subject for junior and high school.

· Data of student available on the detail education units.· Data of number of classroom, furniture, laboratory,

educational media, sport equipment available on theeducation units.

· Data of library on the respective educational unit.· Data of school location respectively using GIS.· Data of incoming student registration for the trans

parency and accountability of incoming students enrollment.

2. Unified data from the respective educational unit entirely on the Mataram local government level especiallythe Education Service of Mataram.

3. Processed data on the Education Service of Mataramto be an education management information system ofMataram.

D. METHOD OF RESEARCH

1. Kind of Research

The conducted kind of research is survey research thatis by taking samples from population using questionnaires as a suitable data collecting tool (Singarimbun,1989). This survey research is for explanatory or confirmatory in which provides explanation on the relation ofinter-variables through research and examination as formulated previously.

2. Location of Research Research is located on schoolsin Mataram covering state and private elementaryschools which data ac cess directly to the KCDs, andthe state and private junior and high schools whichdirectly accessed to the Education Service of Mataram.

3. Procedure of Data Collecting and ProcessingData is collected in the way of:

· Interview with source who is a leader such as schoolmaster, head of education branch office, or educationservice head-office of Mataram.

· Documentation, provided is books contained procedures and rules of education equipment and infrastructures, functional positions, etc.

· Questionnaires, disseminated on the Education Service of Mataram prior to wireless network preparation,the questionnaires will be containing questions of dataflow diagram, hardware or software used, etc.

· Observation, learning the data flow diagram. It is implemented from data collecting, processing until documenting and reporting processes.

4. Data Analysisa. System Planning

It uses methodology of System Development LifeCycle (SDLC) by structured and prototyping techniques. Presently as analyzed, the education service has some departments having self-working, itmakes the process is taking longer time. This newsystem enables each department of education subsystem connected each other; it makes each of themknow the available information. To keep the datasecurity, there should be ID Number for every department or personnel involved as password to access into the related departments.

b. Validity Examination and Sense Analysis Analysison the information system comprises:

- Filling-out procedure of education data- Processing procedure of education data- Reporting procedure of education data

c. System Analysis, covering:§ Need analysis to produce system need specifica

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tion§ Process analysis to produce:- Context Diagram- Documentation Diagram- Data Flow Diagram (DFD)§ Data analysis to produce:- Entity Relationship Diagram (ERD)- Structure of Data

The system analysis aims to analyze the runningsystem to understand the existing condition. This analy-sis usually uses document flow diagram. The flow of docu-ment from one department to another can be seen obvi-ously, as well as is the manual data savings. This processanalysis is also used on the equipment and infrastruc-tures. Results of this analysis then are used to designinformation system as required, to make an “InformativeSystem Construction “.

E. RESULT AND DISCUSSION

1. Construction of Wireless Network

Chart 1. Construction of Wireless Network

2. Development of Hardware· Addition of the Triangle facility on the Education

Service office of Mataram as the central ofmanagement.

· Addition of antenna and receiver radio at therespective cluster on elementary school, 9 units foreach sub-district and 3 units for the cluster leaders.

· Procurement of 6 units of antenna and receiver radioat the respective sub-district of junior schools forthe sub-district leaders.

· Procurement of 6 units of antenna and receiver radio,2 units for each sub-district of high schools in 2007.

Chart 2.. Hardware network

3. Development of Software· Mataram Area Network (MAN) Base incoming

student enrollment· School base education equipments and

infrastructures· School base functional position of teachers· Geographic Information System (GIS) base

school mapping

4. Development Model Designa. System DevelopmentProblem solution and user needs fulfillment are the mainpurpose of this development. Therefore, in thedevelopment should be noticed the information systemprinciples, those are:

1. Involving the system users.2. Conducting work phases, for easier management and

improving effectiveness.3. Following the standard to maintain development

consistency and documentation.4. System development as the investment.5. Having obvious scope.6. Dividing system into a number of subsystems, for

facilitating system development.7. Flexibilities, easily further changeable and improvable.

Besides fulfilling the principles, system developmentshould also apply information system methodology.One of the methodologies and very popular is SystemDevelopment life Cycle (SDLC), using structured andprototyping techniques.

b. ImplementationThis phase is commenced on making database in SQL byconversing database into tables, adding integrateslimitations, making required functions and view to combinetables. Application software is using PHP language toaccess the database. Information system process of creditpoint determining enumeration is the important processon this module.

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a. Means and Infrastructure Information SystemPrime Display

Figure3. Prime Display of SIMAP Mataram

Data Input MenuData Input Menu is used to put means and infrastructurefor elementary schools data by the KCD of the respectivesub-district, while for junior and high schools is done bythe respective available educational unit..As seen at the following Figure:1. Means of education data input:

Figure 4 Means of education data input

2. Data input of teacher profile on the education unit

Figure 5. Educational staff (teacher)

3. Land infrastructure data input

Figure 6. Land infrastructure data

4. Land infrastructure data input

Figure 7. Land infrastructure data

5. Student data input

Figure 8. Student data input

Input design is a data display designed to receivedata input from user as the data entry administrator. Thisinput design should have clarity for users, either of itssort or its data to put in. Meanwhile, input design of theCredit Point Fulfillment System has 2 designs: CurriculumVitae and obtained credit points.

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Figure 9. Teacher/instructor identity data input

Figure 10. Teacher/instructor resume data input

B. Library Information System1. Prime Menu of Library Information SystemIt displays various menus of procurement, processing,tracing, membership and circulation, rule catalogue,administration and security. This menu display could beset-up according to the user access (privilege), such ascould only activate the trace menu for public users, asshown on the Figure below:

Figure 11. Prime menu of Library Information System2. Administration, Security, and Access LimitationThis feature accommodates functions to handle limitationand authority of users. It classifies users and providesthem identification and password. And also it providesself-access management, development, and processingas required.

Figure 12. Key-word facility

3 Reference ProcurementThis feature accommodates functions to record request,ordering, and payment of references, including theacceptance and reporting the procurement process.

Figure 13. Administrator menu facility

4. Reference ProcessingThis feature accommodates input process of book/magazine to the database, status tracing of the processedbooks, barcode input of book/number cover, makingcatalogue card, barcode label, and book call number.

Figure 14. Catalogue category data input facility

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Figure 15. Catalogue data input facility

5. Reference TracingTracing or re-searching of the kept collections is animportant matter in the library. This featureaccommodates tracing through author, title, publisher,subject, published year, etc.

Figure 16. Borrowing data input facility

6. Management of Member and CirculationIt is the center-point of the library automatic system,because here are many manual activities replaced bythe computer. Available inside is various features i.e.input and search of library member data, record ofbook borrowing and returning (using barcodetechnology), fine calculating for delay book return,and book ordering.

Figure 17. Borrower’s return catalogue facility7. Reporting

Reporting system eases librarian to work faster, in whichreport and recapitulation is automatically made asmanaged parameter. It is very helpful in analyzing libraryactivity processes, such as the librarian does not needto open thousands of transaction manually to find thecollection borrowing transaction of one category, or tocheck a member’s activity for a year.

F. CONCLUSIONThe Construction of School Base Education InformationUsing Wireless Network in Mataram is an integratedhardware and software in supporting education, comprisessome modules such as:1. Education equipment and infrastructures such as

land, building, sport field, etc.2. Educators and educational supporting staffs.3. Students data on each educational unit4. Data of teacher’s functional position on education

unit and on level of Mataram Education Service.5. On-line system of Incoming Students Acceptance6. E-learning base learning.

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Anonim, 2005. Peraturan Pemerintah Nomor 19 TentangStandar Nasional Pendidikan (SNP).

Curtis G.1995 Bussines Information System Analysis,Design and Practice,2rdEdition. Addison Wesley.

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Paper

ErmatitaInformation systems of Computer science Faculty Sriwijaya University

(Student of Doctoral Program Gadjah Mada university)[email protected]

Edi WinarkoComputer Science of Mathematics and Natural Sciences Faculty Gadjah Mada University

Retantyo WardoyoComputer Science of Mathematics and Natural Sciences Faculty Gadjah Mada University

AbstractData mining is a process to extract information from a set of data, not the exception of the data in the field bioinformatics.Classification technique which is one of the techniques extract information in data mining, have been much help infinding information to make an accurate prediction. Many of the techniques of research in the field of classificationbioinformatics was done. Research has been the development of methods in the introduction to classify the data patternin the field bioinformatics. Classification methods such as Analysis Discriminant, K-Nearest-neighbor Classifiers,Bayesian classifiers, Support Vector Machine, Ensemble Methods, Kernel-based methods, and linier programming, hasbeen a lot of experience in the development of the output by researchers to obtain more accurate results in the input ofthe data.

Keywords: Bioinformatics, classification, data mining, K - Nearest-neighbor Classifiers, Bayesian classifiers, SupportVector Machine, Ensemble Methods, Kernel-based methods, and linier programming,

Saturday, August 8, 200915:10 - 15:30 Room L-212

A SURVEY OF CLASSIFICATION TECHNIQUES AND APLICATION INBIOINFORMATICS

I. INTRODUCTION

Data mining is defined as the process of automaticallyextract information from a subset of the data was largeand find patterns of interest (Nugroho, U.S., 2008) (Abidin,T, 2008). Classification techniques in data mining has beenapplied in the field bioinformatics [24].

Bioinformatics developed from human needs toanalyze these data that the quantity increases. For thedata mining as one of the techniques have an importantrole in bioinformatics.

In this paper will focus on introducing themethodology in the field of classification in bioinformatics.Activity in the classification and prediction is for a modelthat is able to input data on a new bioinformatics thathave never been there. Classification of data, the data isnot been there.

Model which resulted in a classification is called aclassifier. Some of the models in the classificationbioinformatics data has been in use for example Analysis

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Diskriminant, Decision Tree, Neural Network, BayesianNetwork, Support Vector Machines, k-Nearest Neighbor,etc.. That is used to input data category (diskrit), while forthe numeric data (Numerical data) usually use regressionanalysis. These methods have been applied in manybioinformatics such as the introduction PatterrnRecognition in gene. Many studies have been done forthe development of methods of pattern recognition andclassification for gene expression and microarray data wasdone. Jain, AK (2002) conducted a review of the methodsof statistics, Califano, et all Analysis of Gene ExpressionMicroarrays for Phenotype Classification, Liu, Y (2002),Rogersy, S, doing research on the Class Prediction withMicroarray Datasets. Lai, C (2006) , Lee, G et all (2008)Nonlinear Dimensionality Reduction, Nugroho, AS, et all(2008) of SVM for the microarray data. Every research hasrevealed the results developed to obtain optimal resultsin the classification process.

II. CLASSIFICATION IN THE BIONFORMATICS

Classification models are needed in the classificationthat is also used in kalisifikasi data in bioinformatics. Tobuild a classification model from the input data set requiredan approach which is called classifier or classificationtechniques[32].

Application of data mining in the areas ofbioinformatics for example, to perform feature selectionand classification. Examples such as the implementationof this done by Nugroho, AS, et all (2008), in his researchretrieve data that is divided in two groups: training set (27ALL and 11 AML), and the test set (20 ALL and 14 AML).Each sample consisted of 7129 vectors dimension theexpression of genes derived from the patient as a result ofthe analysis of Affymetrix high density oligonucleotidemicroarray. Both genes are No.7 (AFFX-BioDn-3_at) andNo. 4847 (X95735_at). Distribution of data in the fieldformed by the two genes are as shown in the figure.1.Figure. 1a shows that the data on the second class in thetraining set can separate the linier perfect. Figure 1b showsthat the data on the test set can not separate the linier.data, not the exception of the data in the fieldbioinformatics. Classification technique which is one ofthe techniques extract information in data mining, havebeen much help in finding information to make an accurateprediction. Many of the techniques of research in the fieldof classification bioinformatics was done. Research hasbeen the development of methods in the introduction toclassify the data pattern in the field bioinformatics.Classification methods such as Analysis Discriminant, K-Nearest-neighbor Classifiers, Bayesian classifiers,

Support Vector Machine, Ensemble Methods, Kernel-based methods, and linier programming, has been a lot ofexperience in the development of the output byresearchers to obtain more accurate results in the input ofthe data.

Keywords: Bioinformatics, classification, data mining, K- Nearest-neighbor Classifiers, Bayesian classifiers,Support Vector Machine, Ensemble Methods, Kernel-based methods, and linier programming,

I. INTRODUCTION

Data mining is defined as the process of automaticallyextract information from a subset of the data was largeand find patterns of interest (Nugroho, U.S., 2008) (Abidin,T, 2008). Classification techniques in data mining has beenapplied in the field bioinformatics [24].

Bioinformatics developed from human needs toanalyze these data that the quantity increases. For thedata mining as one of the techniques have an importantrole in bioinformatics.

In this paper will focus on introducing themethodology in the field of classification in bioinformatics.Activity in the classification and prediction is for a modelthat is able to input data on a new bioinformatics thathave never been there. Classification of data, the data isin a particular class label. Form a classification model thatwill be used to predict class labels to new data that havenot been there.

Model which resulted in a classification is called aclassifier. Some of the models in the classificationbioinformatics data has been in use for example AnalysisDiskriminant, Decision Tree, Neural Network, BayesianNetwork, Support Vector Machines, k-Nearest Neighbor,etc.. That is used to input data category (diskrit), while forthe numeric data (Numerical data) usually use regressionanalysis. These methods have been applied in manybioinformatics such as the introduction PatterrnRecognition in gene. Many studies have been done forthe development of methods of pattern recognition andclassification for gene expression and microarray data wasdone. Jain, AK (2002) conducted a review of the methodsof statistics, Califano, et all Analysis of Gene ExpressionMicroarrays for Phenotype Classification, Liu, Y (2002),Rogersy, S, doing research on the Class Prediction withMicroarray Datasets. Lai, C (2006) , Lee, G et all (2008)Nonlinear Dimensionality Reduction, Nugroho, AS, et all(2008) of SVM for the microarray data. Every research has

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revealed the results developed to obtain optimal resultsin the classification process.

II. CLASSIFICATION IN THE BIONFORMATICS

Classification models are needed in the classificationthat is also used in kalisifikasi data in bioinformatics. Tobuild a classification model from the input data set requiredan approach which is called classifier or classificationtechniques[32].

Application of data mining in the areas ofbioinformatics for example, to perform feature selectionand classification. Examples such as the implementationof this done by Nugroho, AS, et all (2008), in his researchretrieve data that is divided in two groups: training set (27ALL and 11 AML), and the test set (20 ALL and 14 AML).Each sample consisted of 7129 vectors dimension theexpression of genes derived from the patient as a result ofthe analysis of Affymetrix high density oligonucleotidemicroarray. Both genes are No.7 (AFFX-BioDn-3_at) andNo. 4847 (X95735_at). Distribution of data in the fieldformed by the two genes are as shown in the figure.1.Figure. 1a shows that the data on the second class in thetraining set can separate the linier perfect. Figure 1b showsthat the data on the test set can not separate the linier.

Figure 1 Distribution of data in the training set (a)and test set (b) in the field formed by gen No.7 and genNo.4847

Golub, TR, et all (1999) has conducted research in theclassification in the field bioinformatics, research in thegeneric approach is used for classification of cancer basedon the monitoring of genes expression through a DNAmicroarray to test the application in human acuteleukemia[8].

Currently classification techniques have beendeveloped mainly in the field bioinformatics, thisdevelopment has done a lot of research. For example

Diskriminant Analysis, Nearest-neighbor Classifiers,Bayesian classifiers, Artificial Neural Network, SupportVector Machine, Ensemble methods and Kernel-basedmethods, and linier programming.

All classification techniques have been developedrapidly, the need for input in the field bioinformatics on aparticular condition, so that the results obtained areaccurate.

III. CLASSIFICATION TECHNIQUES ANDAPPLICATION IN BIOINFORMATICS

3.1 Discriminant Analysis

Discriminant analysis according to Tan, et all (2006)is a classification study in the statistics class. Researchon the classification method diskriminant analysis hasbeen done and gained in the methods that have beendeveloped in bioinformatics. Lee, et all (2008) in his paperhave to experiment to Uncorrelated Linear discriminantAnalysis (ULDA) and Diagonal Linear discriminantAnalysis (DLDA) which is the development of the LDA.The Eksperiment results show that the pattern ofperformance ULDA work less well in the case of smallfeature size and is very good for a number of genes inmany. and vice versa DLDA pattern shows a strongperformance for the small number of features[32].

3.2 K-Nearest-neighbor Classifiers

Classification techniques K-Nearest-NeighborClassifiers are non-parametric classification of groups thathave been in the application in information retrieval (Li, Tet all 2004). Research and development of thisclassification technique has been performed. Aha (1990)in (Tan, PN, 2006) presents theoretical and empiricalevaluation for the instance-based methods, developed byPEBLS Cost (1993) KNN that can handle data setscontaining nominal attributes[32].

In the field of bioinformatics Slonim, DK, et all havebeen doing research to diagnose and treat cancer throughthe expression of genes with the classification method ofneighborhood analysis.

Picture below shows the neighborhood around ahypothetical difference in class c and differences of classrandom c ‘is made in the study.

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Figure 2. hypothetical class c and differences ofclassification of genes expression

Besides Li, T, et all (2004) has conducted studies forfeature selection, accuracy of feature selection increasedsince permitted to eliminate noise and reduce the numberof dimention not significant. Thus, tackling the lack of adimensional. KNN on this case based on the workingdistance between the sample geometry. Accuracy KNNhas been on the increase in all datasets except pa HBCand Lymphoma datasets[18].

KNN number of errors have been corrected can betwo times better than a straight line with the Bayesianerror number.

.3.3 Bayesian classifiers

Bayesian classifiers on classification technique isrelated to the statistical (Statistical classifiers, it is raisedby Santosa, B (2007), Han, J (2001) Tan, PN (2006) and thisapproach may be possible to predict class membership[11].

Krishnapuram, B, et all (2003) revealed that theBayesian approach may help if the classifier is very simple,effectively covering the structure of the basic relationship.Bayesian can help with introducing some type of priorknowledge into the design Pase.

Wang, H (2008) revealed that since the G-based Bayesclassifier is equivalent to the P-based Bayes classifieraccording to Corollary 3, it can be said that the NCMweighted kNN approximates the a P-based Bayes classifier,and in restrictiveness, its performance will be close to P-based Bayes classifier. Consequently. Expectations for theperformance of NCM weighted kNN in practice. On thegrowth of Bayesian belief Network provides a graphicalrepresentation of the relationships between the probabilityof a set of random variables. Bayesian belief Network canbe used in the field bioinformatics to detect heartdisease[33].

Baldi, P. et all (2001) have developed a Bayesianprobabilistic framework for microarray data analysis.Simulation shows that this point estimate with acombination of t-test provides systematic inference

approach that compares well with the simple t-test or Foldmethods[1].

Bayes very good applied to text classification.Additional knowledge that is very good on Bayesian beliefnetworks in the given by Heckerman (1997) in Tan, PN(2006).

3.4 Support Vector MachineSupport Vector Machine classification technique still

relatively new model in classification. This technique hasbeen in use to complete the problems in bioinformatics ingene expression analysis. This technique seeks to findthe classification function separator (clasifier) thatoptimally separates the normal two sets of data from twodifferent classes (vapnik: 1995)

NugrohoA.S (2003) review the SVM as follows:

Figure. 3 SVM tries to find the best hyperplaneseparating the two classes -1 and +1

Picture above shows some pattern that is a memberof two classes: +1 and -1. Pattern joined on the class -1the simbol with the pattern and the class +1 the simbolbox. Classification problem can be translated with the effortto find a line (hyperplane) that separates between the twogroups .

Hyperplane separator between the two classes canbe found with the measure margin hyperplane, and findthe maximum point. Margin is the distance between thehyperplane with the nearest pattern from each class.Pattern is the closest in a support vector. Solid lines in theimage 1-b shows the best hyperplane, which is locatedright in the middle of the second class, while the red andyellow dots that are in a black circle is the support vector.Effort to find the location of the core of this is thehyperplane in SVM process [24].

Many studies have been done for the developmentof SVM techniques that have principles on linier clasifier,from Boser, Guyon, Vapnik, until the development in orderto work on the problem with non linier incorporate theconcept of the kernel trick in the space high dimension.Cristriani and Shawe-taylor (2000), which has been the

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concept of SVM and kernel to solve the problem inclassification. Fung and Mangasarian (2002) havedeveloped a SVM with the Newton method for featureselection in the Name Newton Method for Linierprogramming SVM (NLPSVM), in this method requiresonly an algorithm of solving the problem linier quicklyand easily at access that can be effectively applied indimensional input spaces such as a very knowledgeablemicroarray. This can be applied in the case of geneexpression data analysis. In addition, this technique canbe used effectively for classification of large data sets inthe input space dimension is smaller (Fung, G; 2002). Andseveral evaluations have been made to this method in itsapplication in the field of bioinformatics. Krishnapuram,et all (2003) have developed a Bayesian generalization ofthe SVM was optimized to identify with andsimultaneously non linier classifier and optimal selectionof cells through the optimization feature Single Bayesianlikelihood function. Bredensteiner (1999) show how theapproach Linier Programming (LP) based approach basedon quadratic Programming SVM can be combined into anew approach to multiclass problem [14].

Dönnes (2002) have used the SVM approach topredict bond of peptides to Major HistocompatibilityComplex (MHC) class I molecules, this approach is calledSVMMHC[8].

On development or engineering approach, SVM hasbeen widely used for various job classifications,particularly in the areas of bioinformatics for data analysisand feature selection.

(Liu, 2005) combines genetic algorithm (GA) and AllPaired (AP) support Vector Machines categorize methodsfor multiclass cancer. Predictive features can beautomatically determined through iterative GA / SVM,leading to very compact sets of nonredundant cancergenes-Relevant Classification with the best performancereported to date[21]

Cai describe a method for classification normal andcancer based on the pattern genes expression obtainedfrom DNA microarray experiment in this research he wascomparing two supervised machine learning techniques,support vector machines and decision tree algorithm[3].

3.5 Ensemble MethodsThis technique is to improve the classification accuracy

of the prediction multiple-input classifier. Figure belowis a technical overview Ensemble [5] . If there are kfeatures and n classifiers, and k × n feature-classifiercombinations. There is a k × nCm possibility Ensembleclassifiers when the m-feature classifier combinationsselect for the Ensemble classifier. Then the trainedclassifiers using the features in the select, the lastaccompanied a majority voting to combine theseclassifiers outputs. after several features with

classifiers trained on the output they are independent,the final answer will be determined by a combinationof modules, where the majority voting method in theadoption.

Figure 4 Overview of the Ensemble classifier In the field of bioinformatics, Kim (2006) adopted

the correlation Analysis of feature selection methods inthe Ensemble classifier used for classification of DNAmicroarray [13].

3.4 Kernel –based MethodWhen a case shows the non linier classification,

difficult to separate the linier, kernel method can introducethem in by Scholkopf and Smola (2002). Method with akernel in the input data x in the space mapped to featurespace F with a higher dimension through mapö as follows:ö: x ö (x). Because the data in the input space x to be ö (x)in feature space [28].

Many research has done in the kernel method withthe methods that have been there before. As the kernel K-Means, kernel PCA, kernel LDA. Kernelisasi method canimprove the previous method.

Huilin (2006) modify the KNN in kernel techniques toperform classification in bioinformatics cancer. Hedeveloped a novel distance metric in KNN scheme forcancer classification. The substance of increasing classseparability of data in feature space and significantlyimprove the performance of KNN.

3.5 Linier Programming

Mangasarian, et all (1994) has conducted researchusing linier programming to perform the classification onthe breast cancer accuraacy and to improve objectivity inthe diagnosis and prognosis of cancer. By using themethod of classification based linier programming has builta system that has high diagnosis accuracy for the surgicalprocedure.

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IV. CONCLUSION

Techniques that have been developed in data miningtechniques such as classification has been helping manyproblems in the field bioinformatics.

In microarray data analysis has been done manystudies to obtain optimal results in the classification. Thisis to obtain accurate data to obtain a valid analysis.Classification approach in the most developed at this timeis the support vector machine in the algorithm to combinewith the other algorithms, to produce optimal results.

Reference

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[17] Ljubomir, J. Buturovic, “PCP: a program for supervisedclassification of gene expression profiles”,Bioinformatics, Vol. 22 , No. 2 , pp: 245–247, 2006,doi:10.1093/bioinformatics/bti760

[18] Li, H.L. ,J. and Wong, L, “A Comparative Study onFeature Selection and Classification Methods UsingGene Expression Profiles and Proteomic Patterns”,Genome Informatics 13: 51{60} ,2002, availableonh t tp : / / c i t e see rx . i s t . p su . edu /v i ewdoc /summary?doi=10.1.1.99.6529

[19] Li, T. Zhang, C. and Ogihara, M. “A comparativestudy of feature selection and multiclassclassification methods for tissue classificationbased on gene expression”, Bioinformatics, Vol. 20,No. 15, pages 2429–2437, 2004, doi:10.1093/bioinformatics/bth267

[20] Liu, Y. “The Numerical Characterization and SimilarityAnalysis of DNA Primary Sequences”, InternetElectronic Journal of Molecular Design 2002, ,Volume 1, Number 12, Pages 675–684, December2002

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[23] Mangasarian,O.L, Street,W.N, Wolberg,W.H, BreastCancer Diagnosis and Prognosis via LinierProgramming, 1994, computer sciencesDepartemen, University of Wiconsin,USAhttp://c i t e s e e r x . i s t . p s u . e d u / v i e w d o c /s u m m a r y ? d o i = 1 0 . 1 . 1 . 1 3 3 . 2 2 6 3

[24] Nugroho, A.S, Witarto, A.B dan Handoko, D, “SupportVector Machine: Teori dan aplikasinya dalamBioinformatika, http://ilmukomputer.com, Desember2008 (in Indonesian)

[25]Nowicki, R. “On Combining Neuro-FuzzyArchitectures with the Rough Set Theory to SolveClassification Problems with Incomplete Data”,IEEE Transactions on Knowledge and DataEngineering, VOL. 20, No. 9, pp:1239-1253, Sep2008

[26] Robnik, M. Sikonja, Member, IEEE, and I. Kononenko,“Explaining Classifications for IndividualInstances”, IEEE Transactions on Knowledge andData Engineering, VOL. 20, No. 5, pp:589-600, May2008

[27] Rogersy, S, Williamsz R. D, and Campbell, C, “ClassPrediction with Microarray Datasets” available onh t t p : / / c i t e s e e r x . i s t . p s u . e d u / v i e w d o c /summary?doi=10.1.1.97.3753

[28] Santosa, B. “Data Mining: Technical data for thepurpose of the business theory and application”,Graha Ilmu, Yogyakarta, 2007

[29] G.P. and Grinstein, G, “A Comprehensive MicroarrayData Generator to Map the Space of Classificationand Clustering Methods”, june 2004, available onh t t p : / / c i t e s e e r x . i s t . p s u . e d u / v i e w d o c /summary?doi=10.1.1.99.6529

[30] Slonim,D.K, Tamayo.P dkk, Class Prediction andDiscovery Using gene Expression Data,2000.http:// c i t e s e e r x . i s t . p s u . e d u / v i e w d o c /summary?doi=10.1.1.37.3435

[31] Statnikov, A. Aliferis, C. F. Tsamardinos, I. Hardin, D.and Levy, S. “ A comprehensive evaluation ofmulticategory classification methods for microarraygene expression cancer diagnosis”, Bioinformatics,Vol. 21 no. 5, pages 631–643 , 2005 doi:10.1093/bioinformatics/bti033

[32] Tan, P.N. Steinbach, M.. and Kumar, V. “IntruductionTo Data Mining”, Pearson Education,Inc, Boston,2006

[33] Wang, H. and Murtagh, F. “A Study of theNeighborhood Counting Similarity”, IEEETransactions on Knowledge and Data Engineering,VOL. 20, No. 4, pp:469-461, April 2008

[34] Xiong, H. and. Chen, X “Kernel-based distance metriclearning for microarray data classification”, BMC hBioinformatics, 7:299, 2006, doi:10.1186/1471-2105-7-299 available from: http://www.biomedcentral.com/1471-2105/7/299

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Paper

Information Technology Department – Faculty of Computer Study STMIK RaharjaJl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

[email protected], [email protected]

ABSTRAKSIDesktop is something that is not foreign for computer users, is a form of the display screen as a medium for the operationof the operating system-based gui. Linux operating system with all turunannya has inherent with the use of multi-desktop,in which a user can have multiple active desktop at the same time. This may be necessary to make it easier to for users tobe able to mengelompokan some of the applications that are opened, so it does not look untidy. However, for usersoperating system based windows, multi desktop is not found in the operation. Use visual basic to the ability to access thewindows in the fire to be able to create an application that will create multiple windows on the desktop as well as themulti-desktop on linux. This is necessary, because it is not uncommon for Windows users feel confused when manyapplications are opened at the same time, because the desktop does not appear regularly with the number of applicationsthat are running. This paper will discuss the technical implementation of multi desktop linux on windows xp mediaprogramming using visual basic and access the commands in the windows api, active in the notification area icon to theinactive, has its own task manager with the applications that are displayed according to the applications that run ontheir - their desktop. Capabilities designed to create a 10 on a user’s desktop, this has exceeded the ability of new linuxdesktop display 4. Pengujiannya in this application is provided that is capable of 10 desktop is created and run onwindows xp, but in the design stage, this application is able to create the number of desktops that are not limited, this isvery dependent of the amount awarded in accordance with their needs.

Keywords: Multi Desktop, Windows, Linux, Notification Area, Inactive Icon

Saturday, August 8, 200916:25 - 16:45 Room AULA

Engineering MULTI DEKSTOP LINUX ON WINDOWS XP USING THE APIProgramming – VISUAL BASIC

Junaidi, Sugeng Santoso, Euis Sitinur Aisyah

INTRODUCTIONMulti desktop, not something new for some computer us-ers, especially for those already familiar with the operatingsystem and linux turunannya. However, multi-operatingsystem for desktop windows spelled still rare and difficultto get, especially when talking pembuatannya.

Starting in the habit of using the computer and alwaysclose the application that is running when you want toopen a new application, this is not because dilatar belakangiwith many display applications that are open to appear onthe desktop, in addition also the taskbar will be filled withthe name of the application that is currently active, seemedso disorganized and confusing. Not to mention the abilityof the computer that has limited memory and processor,

cause the computer to run slowly. Also self-taught andnever try to install Mandrake and redhat linux on dualboot, and when it is know that the linux desktop is able tocreate four one-on user with the ability to record an openapplication appropriate location of each desktop.

Due to the windows operating system and coincidenceare steep maximum visual basic capabilities, start trying tothink how to apply techniques multi desktop linux on win-dows, of course with the help of the library windows API,disinilah initial interest and seriousness to create a simpleapplication that is capable of running on windows quickly,as well as help in overcoming the complexity because theyare not familiar with the many applications that are active

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in the windows, called naama JaMuDeWi (Junaidi MultiWindows Desktop).

In short, the program is simple JaMuDeWi made with vi-sual basic programming language and use the windowsapi to access some functions of the windows, and will runover the windows operating system (in this case usingvisual basic 6 and windows xp). At the time the program isexecuted, will be active in the notification area icon to theinactive, has a task manager with the application itself isdisplayed according to the applications that run on their -their desktop. Capabilities designed to create a 10 on auser’s desktop, this has exceeded the ability of new linuxdesktop display 4. To be able to move desktop can bedone with the mouse over the icon JaMuDeWi in the noti-fication area, and then do right click to display the desktopmenu. To close this application can also be done in thesame way, and then select the exit, proceed to determinewhether the choice of active applications on the desktopwill be closed or remain with the move to the main desktop.

Discussion

Engineering multi desktop on windows XP with applyingthe concept of multi desktop linux in perancangannya canuse the programming language visual basic, and the win-dows API.

Application of the concept of Multi Desktop

Basically, the concept of multi desktop windows to applythe concept of multi desktop linux that has the ability 4active desktop in a single user, but this program is de-signed to have the ability 10 active desktop in a singleuser, and can be developed as you wish. There are somebasic things that should be involved in perancangannyato create some capacity in support multi desktop windows,which are:

Accessing the Windows API Capabilities

Windows API is very involved in this application, otherthan to switch between the desktop, also needed to hidethe active application on the desktop is not active, anddisplay the active applications on the desktop. It also isable to hide the active application on the desktop is notactive so do not look at the task manager on the desktop isactive. 1 shown in the image there is a declaration in orderto access the windows API. Note the image 1 as the formof a snippet of the script to access the windows api, andthen processed according to need.Capabilities Application Running On With Inactive Notifi-cation Area Icon

Picture 2Layar Design Coding Dalam Mengakses Windows Api The ability to create and run inactive icon in notificationarea aim to be multi desktop application is still accessibleto every desktop is selected, more than that also, it onlyhas a menu interface as the main form and the dialog inter-face to determine the status of the application that openswhen you want to exit, screen interface and dialogue toconvey the information. Note 2 for a picture notificationarea.

Creating Array Capability

To be able to maintain that every application on each desk-top, to be able hidden on the desktop when not selected,and display applications at the desktop is selected, it isnecessary to create an array variable 1 (one) dimensionsto accommodate desktop desktop, and a variable array of2 (two ) dimensions to accommodate the information alongwith desktop applications that are active in their - theirdesktop.

Showing the ability Menyenbunyikan And Applications

Gambar 3Potongan Scrip Dalam Menyembunyikan Aplikasi

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Kemampuang is no less important role, because basicallyall the applications remain active open, but not displayedor hidden entirely. This bias is applied as each applicationwill be stored on the information on the aray provided inaccordance with the location of the desktop applicationwas first run. This capability is impressive as if - in theirmanner - each desktop has its own application, on theactual application is to stay hidden or shown, on its appli-cation to determine which will be shown or hidden is veryclosely related with the location of desktop lines. Watch asnippet of the script in the image hiding in the application3.

Array Manipulation Capabilities and Task Manager

Gambar 4Potongan Scrip Dalam Mendeklarasikan Array

The ability to manipulate an array of arrays isnecessary because the ones who save each desktop alongwith application information. So that its implementation inorder to display the application in accordance with theactive desktop to read the information stored in the ad-dress of each array. And ability to manipulate task man-ager is the application that are hidden will not be visible inthe task manager, otherwise apalikasi shown akan seen inthe task manager where the location of desktop task man-ager is opened. Figure 4 is a snippet in the script to createsome array.

Kemampuan – kemampuan diatas mutlak harusdipenuhi agar aplikasi multi desktop yang dimaksud dapatberjalan dengan sempurna. Masing – masing kemampuanmemiliki saling keterkaitan dengan kemampuan yanglainnya. Misalnya saja kemampuan dalam menyembunyikandan menampilkan aplikasi sesuai dengan lokasi desktopdapat dilakukan dengan memanfaatkan informasi yangtersimpan pada array, dan informasi pada array terealisasiberkat kemampuan dalam manipulasi array.IMPLEMENTASI

Paparan berikut ini akan menampilkan secara fullsource code dari program JaMuDeWi (JunAidi MUltiDEsktop WIndows) yang berhasil di rancang (Gambar 5)

Gambar 5Icon Menu JaMuDeWi

Untuk dapat menciptakan aplikasi multi desktop windowsyang kita beri nama JAMU DEWI menggunakan 1 buahproject dengan 2 form dan 1 buah modul. Dua form yangdimaksud terdiri dari form untuk memilih desktop dan formuntuk dialog keluar. Form pertama yang dimaksudkan untukmemilih desktop yang akan dijalankan terdari dari satumenu utama dengan 10 sub menu untuk memberikan pilihandesktop dari 1 s/d 10 dan 1 sub menu untuk memilih dialogkeluar dari program JAMU DEWI. Form kedua dimaksudkanuntuk dialog keluar teridri dari 1 label untuk memberikanteks pertanyaan aksi setelah keluar dan satu buah combobox yang berisi pilihan Ya dan Tidak sebagai bentukimplementasi jawaban yang ditanyakan pada label yangdimaksud tadi, kemudian terdapat juga 2 command bottomuntuk menangkap pernyataan akhir dari proses keluar yangakan sebagai bentuk pernyataan user bahwa proses keluardibatalkan dengan mengabaikan pilihan pada combo box,dan command bottom kedua yang berisi pernyataan bahwauser setuju untuk keluar dari program aplikasi JAMU DEWIdengan memperhatikan pilihan pada combo box. PilihanYa pada combo box akan melaksanakan perintah untukmemindahkan semua aplikasi yang berjalan disemua desk-top ke desktop utama, sedangkan pilihan kedua

Design JaMuDeWi (JunAidi Multi Desktop Windows)Bahasa pemrograman yang digunakan adalah vi-

sual basic dengan kemampuan mengakses windows api.Aplikasi ini membutuhkan sebuah form utama untukkeperluan menu, sebuah form keluar (Gambar 6) sebagaimedia dialog untuk menentukan aksi lanjutan yang akandilakukan setelah keluar, sebuah form untuk mediainformasi dan sebuah modul untuk membuat beberapacoding untuk keperluan programmer.

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Form Utama JaMuDeWi (frmJaMuDeWi)

Picture 6Layar Informasi JaMuDeWi

Perhatikan coding berikut ini, terdapat beberapadeklarasi variable dengan beberapa prosedur yangdirancang di area coding pada form utama.

‘—frmJaMuDeWi‘— prosedur yang dilakukan pada saat program dijalankanPrivate Sub Form_Load() ‘— Hide this form Me.Hide

‘— variabel penampung informasi desktop aktif intDesktopAktif = 1 intDesktopTerakhir = 1

‘— pengaturan program agar sebagai system tray padatoolbar With NotifyIcon .cbSize = Len(NotifyIcon) .hWnd = Me.hWnd .uId = vbNull .uFlags = NIF_ICON Or NIF_TIP Or NIF_MESSAGE .uCallBackMessage = WM_MOUSEMOVE .hIcon = Me.Icon .szTip = “Klik Kanan - JunAidi MUlti DEsktopWIndows” & vbNullChar End With Shell_NotifyIcon NIM_ADD, NotifyIconEnd Sub

Coding diatas merupakan prosedur yang paling pertamadijalankan pada saat program pertama kali dijalankan danberada pada form utama. Hal ini dilakukan agar programberjalan secara hidden dan muncul icon tray pada pojokkanan bawah. Perintah Me.Hide berfungsi untukmenyembunyikan form dan perintah with notifyIcon …end with berfungsi agar program berjalan dengan systemtray. Terdapat juga deklarasi variable bertipe integer untukmenampung jumlah desktop yang telah dipilih variableuntuk menampung desktop mana yang sedang aktif daribeberapa desktop yang dipilih.

‘— prosedur yang dilakukan pada saat mouse diarahkanke icon programPrivate Sub Form_MouseMove(Button As Integer, ShiftAs Integer, X As Single, Y As Single) ‘— pengaturan agar program berjalan minimize ‘— pengaktifan program dengan click kanan mouse Dim Result As Long Dim Message As Long If Me.ScaleMode = vbPixels Then Message = X Else Message = X / Screen.TwipsPerPixelX End If If Message = WM_RBUTTONUP Then Result = SetForegroundWindow(Me.hWnd) Me.PopupMenu Me.mnu_1 End IfEnd Sub

Coding diatas merupakan bagian dari coding form utamadan berfungsi sebagai prosedur untuk menangkappergerakan mouse pada saat cursor mouse berada tepat diarea icon tray JaMuDeWi. Prosedur ini berfungsi untukmenampilkan pesan singkat tentang keterangan program,dan pengaturan penggunaan tombol kanan mouse.WM_RBTTOMUP berfungsi untuk menampilan menupada saat tombol kanan mouse dilepaskan setelah ditekan.

‘— prosedur yang dijalankan ketika program menampilkanformPrivate Sub Form_Resize() ‘— sembunykan form jika berjalan secara minimize If frmJaMuDeWi.WindowState = vbMinimized Then frmJaMuDeWi.Hide End IfEnd Sub

Coding diatas merupakan bagian dari coding form utamadan berfungsi sebagai prosedur untuk pengaturan pro-gram agar berjalan secara minimize dan disembunyikan agarsystem tray berfungsi.

‘— prosedur yang dijalankan ketika ingin keluar dari pro-gramPrivate Sub Form_Unload(Cancel As Integer) ‘— mematikan system tray icon pada toolbar Shell_NotifyIcon NIM_DELETE, NotifyIconEnd Sub

Coding diatas merupakan bagian dari coding form utamadan berfungsi sebagai prosedur untuk menghapus iconsystem tray pada saat keluar dari program.

‘— pengaturan menu untuk mengkases setiap desktop

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‘— prosedur menu pemilihan desktop J / 1Private Sub mnu1_Click() funPilihDesktop intDesktopAktif, 1End Sub

Coding diatas merupakan bagian dari coding form utamadan berfungsi sebagai prosedur untuk memanggil fungsipemilihan desktop dengan mengirimkan informasi desk-top yang aktif sesuai nilai pada variable sekaligusmengirimkan informasi nomor desktop 1 yang diaktifkansesuai dengan pilihan menu nomor 1.

‘— prosedur menu pemilihan desktop U / 2Private Sub mnu2_Click() funPilihDesktop intDesktopAktif, 2End Sub

‘— prosedur menu pemilihan desktop N / 3Private Sub mnu3_Click() funPilihDesktop intDesktopAktif, 3End Sub

‘— prosedur menu pemilihan desktop A / 4Private Sub mnu4_Click() funPilihDesktop intDesktopAktif, 4End Sub

‘— prosedur menu pemilihan desktop I / 5Private Sub mnu5_Click() funPilihDesktop intDesktopAktif, 5End Sub

‘— prosedur menu pemilihan desktop D / 6Private Sub mnu6_Click() funPilihDesktop intDesktopAktif, 6End Sub

‘— prosedur menu pemilihan desktop I / 7Private Sub mnu7_Click() funPilihDesktop intDesktopAktif, 7End Sub

‘— prosedur menu pemilihan desktop J / 8Private Sub mnu8_Click() funPilihDesktop intDesktopAktif, 8End Sub

‘— prosedur menu pemilihan desktop U / 9Private Sub mnu9_Click() funPilihDesktop intDesktopAktif, 9End Sub

‘— prosedur menu pemilihan desktop N / 10Private Sub mnu10_Click()

funPilihDesktop intDesktopAktif, 10End Sub

‘— prosedur menu keluar untuk menampilkan aksi pilihankeluarPrivate Sub mnuExit_Click() Load frmKeluar frmKeluar.ShowEnd Sub

Coding diatas merupakan bagian dari coding form utamadan berfungsi sebagai prosedur untuk mengaktifkan desk-top yang diinginkan sesuai dengan nama desktop masing-masing. Setiap menu yang ditekan akan menjalani perintahyang berada pada prosedur menu sesuai dengan dalamfungsi pemilihan desktop dengan mengirimkan informasidesktop yang aktif sesuai nilai pada variable sekaligusmengirimkan informasi nomor desktop 1 yang diaktifkansesuai dengan pilihan menu nomor 1.

Form Keluar (frmKeluar)

Picture 7Layar Dialog JaMuDeWi Untuk Aksi Keluar

Selain menggunakan form utama, perlu jugamenyiapkan sebuah form lagi untuk keperluan layar dialogkeluar dari program (Gambar 7). Didalamya terdapat satubuah label yang berisikan pertanyaan aksi yang akandilakukan setelah keluar dari aplikasi, dan satu buah combobox untuk memberikan alernatif pilihan aksi, sertamenggunakan dua buah command bottom.

‘— procedure penekanan tombol keluar untukmenghentikan programPrivate Sub cmdKeluar_Click()

‘— pengaturan variabel untuk pendataan jumlah desk-top dan windows Dim intJumlahDesktop As Integer Dim intJumlahWindow As Integer

‘— aksi yang dilakukan ketika keluar dilakukan If cboAksiKeluar.Text = “Ya” Then

‘— seluruh aplikasi aktif akan dipindahkan ke desk-top utama intJumlahDesktop = 1

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While intJumlahDesktop < 10 intJumlahWindow = 0 While intJumlahWindow <aryJumlahBukaWindows(intJumlahDesktop) RetVal =ShowWindow(aryBukaWindows(intJumlahDesktop,intJumlahWindow), _

SW_SHOW) intJumlahWindow = intJumlahWindow + 1 Wend intJumlahDesktop = intJumlahDesktop + 1 Wend Shell_NotifyIcon NIM_DELETE, NotifyIcon End ElseIf cboAksiKeluar.Text = “Tidak” Then

‘— seluruh aplikasi aktif akan ditutup intJumlahDesktop = 2 While intJumlahDesktop < 10 intJumlahWindow = 0 While intJumlahWindow <aryJumlahBukaWindows(intJumlahDesktop) RetVal =SendMessage(aryBukaWindows(intJumlahDesktop,intJumlahWindow), _

WM_CLOSE, 0, 0) intJumlahWindow = intJumlahWindow + 1 Wend intJumlahDesktop = intJumlahDesktop + 1 Wend Shell_NotifyIcon NIM_DELETE, NotifyIcon End End IfEnd Sub

Coding diatas merupakan bagian dari coding form keluardan berfungsi sebagai layar dialog untuk menentukan aksiapa yang akan dilakukan ketika berhasil keluar dari pro-gram. Pada coding diatas juga terdpat beberapa barisperintah untuk mendeklarasikan beberapa variable desk-top dan aplikasi, terdapat beberapa baris perintah untukmelakukan langkah-langkah untuk memindahkan aplikasiyang terbuka ke menu desktop utama atau sebaliknya.

‘— prosedur penekanan tombol batal untuk keluarPrivate Sub cmdBatal_Click() ‘— keluar program Unload MeEnd Sub

Coding diatas merupakan bagian dari coding form keluaryang merupakan aksi atas penekanan tombol batal yangdisediakan.

‘— prosedur yang dilakukan pada saat program keluardijalankanPrivate Sub Form_Load() ‘— pengaturan awal posisi windows SetWindowPos Me.hWnd, HWND_TOPMOST, 0, 0, 0,0, SWP_NOMOVE Or SWP_NOSIZE

‘— pengisian combobox dengan aksi pilihan keluar cboAksiKeluar.AddItem “Ya” cboAksiKeluar.AddItem “Tidak” cboAksiKeluar.Text = “Ya”End Sub

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

Modul JaMuDeWi (mdlJaMuDeWi)

Picture 8Potongan Coding Deklarasi API

Untuk dapat lebih memaksimalkan danmenjalankan program ini sesuai dengan fungsinya, makamenyiapkan sebuah modul sebagai bentuk komunikasidengan windows api. Diantanya akan memanggil beberapafungsi api dengan deklarasi publik, seperti FungsiShowWindows, GetWindows, SetWindows,SetForeground, SetMessage, SendMessage,SetNotufyIcon dan masih banyak lagi sesuai kebutuhan.Yang terpenting dari sini adalah, setiap api yang dipanggilmerujuk kepada library tertentu untuk memanfaatkanbeberapa fungsi, seperti penggunaan library user32, danlain sebagainya. Berikut adalah coding dari modul dalammemanfaatkan windows api yang dimaksud. Potonganscript modul dapat dilihat pada gambar 8.

‘— deklarasi pemanggilan fungsi API WindowsPublic Declare Function ShowWindow _Lib “user32” (ByVal hWnd As Long, ByVal nCmdShow AsLong) _As LongPublic Declare Function GetWindow _Lib “user32” (ByVal hWnd As Long, ByVal wCmd As Long)

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_As LongPublic Declare Function GetWindowWord _Lib “user32” (ByVal hWnd As Long, ByVal wIndx As Long)_As LongPublic Declare Function GetWindowLong _Lib “user32” _Alias “GetWindowLongA” (ByVal hWnd As Long, ByValwIndx As Long) _As Long

Public Declare Function GetWindowText _Lib “user32” _Alias “GetWindowTextA” (ByVal hWnd As Long, ByVallpSting _As String, ByVal nMaxCount As Long) As LongPublic Declare Function GetWindowTextLength _Lib “user32” _Alias “GetWindowTextLengthA” (ByVal hWnd As Long)_As LongPublic Declare Function SetWindowPos _Lib “user32” _(ByVal hWnd As Long, _ByVal hWndInsertAfter As Long, _ByVal X As Long, _ByVal Y As Long, _ByVal cx As Long, _ByVal cy As Long, _ByVal wFlags As Long) _As LongPublic Declare Function SetForegroundWindow _Lib “user32” (ByVal hWnd As Long) _As LongPublic Declare Function PostMessage _Lib “user32” _Alias “PostMessageA” _(ByVal hWnd As Long, _ByVal wMsg As Long, _ByVal wParam As Long, _lParam As Any) _As LongPublic Declare Function SendMessageByString _Lib “user32” _Alias “SendMessageA” _(ByVal hWnd As Long, _ByVal wMsg As Long, _ByVal wParam As Long, _ByVal lParam As String) _As LongPublic Declare Function SendMessage _Lib “user32” _Alias “SendMessageA” _

(ByVal hWnd As Long, _ByVal wMsg As Long, _ByVal wParam As Integer, _ByVal lParam As Long) _As LongPublic Declare Function _Shell_NotifyIcon _Lib “shell32” _Alias “Shell_NotifyIconA” _(ByVal dwMessage As Long, _pnid As NOTIFYICONDATA) _As Boolean

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

‘— deklarasi tipe data publicPublic Type NOTIFYICONDATA cbSize As Long hWnd As Long uId As Long uFlags As Long uCallBackMessage As Long hIcon As Long szTip As String * 64End Type

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

‘— deklarasi variabel public ConstantsPublic Const SWP_NOMOVE = 2Public Const SWP_NOSIZE = 1Public Const HWND_TOPMOST = -1Public Const HWND_NOTOPMOST = -2Public Const GW_HWNDFIRST = 0Public Const GW_HWNDNEXT = 2Public Const GWL_STYLE = (-16)Public Const NIM_ADD = &H0Public Const NIM_MODIFY = &H1Public Const NIM_DELETE = &H2Public Const NIF_MESSAGE = &H1Public Const NIF_ICON = &H2Public Const NIF_TIP = &H4Public Const SW_HIDE = 0Public Const SW_MAXIMIZE = 3Public Const SW_SHOW = 5Public Const SW_MINIMIZE = 6Public Const WM_CLOSE = &H10Public Const WM_MOUSEMOVE = &H200Public Const WM_LBUTTONDOWN = &H201Public Const WM_LBUTTONUP = &H202

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Public Const WM_LBUTTONDBLCLK = &H203Public Const WM_RBUTTONDOWN = &H204Public Const WM_RBUTTONUP = &H205Public Const WM_RBUTTONDBLCLK = &H206Public Const WS_VISIBLE = &H10000000Public Const WS_BORDER = &H800000

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

‘— array untuk menampung 10 informasi desktop

‘— array 2 dimensi untuk menampung aplikasi yang terbukapada setiap desktopPublic aryBukaWindows(0 To 10, 0 To 1023) As Long

‘— array 1 dimensi untuk menampung jumlah desktop yangbisa dibukaPublic aryJumlahBukaWindows(0 To 10) As Long

‘— variabel untuk menampung nomor desktopPublic intDesktopAktif As IntegerPublic intDesktopTerakhir As Integer

‘— pengaturan variabel typePublic NotifyIcon As NOTIFYICONDATAPublic IsTask As Long

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

‘— fungsi untuk penanganan pemilhan desktopPublic Function funPilihDesktop(intDesktopAsal As In-teger, intDesktopTujuan As Integer) ‘— variabel penampung untuk penangan windows dandesktop Dim hwndPilihWindows As Long Dim intPanjang As Long Dim strJudulWindow As String Dim intJumlahWindow As Integer

‘— setiap ingin berpindah desktop, lakukan cek padatawsk untuk setiap windows aktif ‘— jika berada pada desktop terpilih tampilkan, jika tidaksembunyikan IsTask = WS_VISIBLE Or WS_BORDER intJumlahWindow = 0 hwndPilihWindows =GetWindow(frmJaMuDeWi.hWnd, GW_HWNDFIRST) Do While hwndPilihWindows If hwndPilihWindows <> frmJaMuDeWi.hWnd AndTaskWindow(hwndPilihWindows) Then

intPanjang =GetWindowTextLength(hwndPilihWindows) + 1 strJudulWindow = Space$(intPanjang) intPanjang = GetWindowText(hwndPilihWindows,strJudulWindow, intPanjang) If intPanjang > 0 Then If hwndPilihWindows <> frmJaMuDeWi.hWndThen RetVal = ShowWindow(hwndPilihWindows,SW_HIDE) aryBukaWindows(intDesktopAsal,intJumlahWindow) = hwndPilihWindows intJumlahWindow = intJumlahWindow + 1 End If End If End If hwndPilihWindows =GetWindow(hwndPilihWindows, GW_HWNDNEXT) Loop aryJumlahBukaWindows(intDesktopAsal) =intJumlahWindow

‘— tampilkan desktop terpilih ke paling atas ‘— didapat dari informasi aray berdasarkan desktopyang terakhir dibuka ‘— secara default isi array adalah kosong intJumlahWindow = 0 While intJumlahWindow <aryJumlahBukaWindows(intDesktopTujuan) RetVal =ShowWindow(aryBukaWindows(intDesktopTujuan,intJumlahWindow), _SW_SHOW) intJumlahWindow = intJumlahWindow + 1 Wend

‘— memindahkan dari desktop aktif / terpilih ke desktopbaru / dipilih intDesktopTerakhir = intDesktopAsal intDesktopAktif = intDesktopTujuanEnd Function

Coding diatas merupakan bagian dari coding form keluaryang akan dijalankan pada saat form keluar pertama kalidijalankan.

Function TaskWindow(hwCurr As Long) As Long ‘— panangan windows untuk keperluan task manager Dim lngStyle As Long lngStyle = GetWindowLong(hwCurr, GWL_STYLE) If (lngStyle And IsTask) = IsTask Then TaskWindow =TrueEnd Function

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Coding diatas diperlukan guna pengaturan task manager.Setiap desktop memiliki list task manager sendiri, dengankata lain aplikasi yang aktif bukan pada lokasi desktopyang dimaksud akan disembunyikan, sebaliknya aplikasiyang dibuka pada lokasi desktop dimana task managerdiaktifkan akan ditampilkan.KESIMPULAN

Dalam pengujiannya aplikasi ini memang disediakan 10desktop yang mampu diciptakan dan berjalan pada win-dows xp, namun demikian pada tahap perancangan, aplikasiini mampu menciptakan jumlah desktop yang tidakterbatas, hal ini sangat tergantung dari jumlah yangdiberikan sesuai dengan kebutuhan.

Prinsip kerja dari multi desktop windows ini adalah denganmenyiapkan sebuah array ber dimensi satu untukmenampung informasi desktop dan array berdimensi duauntuk menampung informasi aplikasi yang aktif pada setiapdesktop. Kemudian dilakukan manipulasi denganmenyembunyikan atau menampilkan aplikasi yang aktifsesuai dengan desktop yang dipilih atau desktop yangtidak terpiliah.

Penggunaan perintah API Windows pada pemrogramanvisual basic untuk mengakses beberapa fungsi windowsdapat memaksimalkan fungsi visual basic itu sendiri,sehinga aplikasi multi desktop windows sebagai konseppenerapan dari multi desktop linux telah mampu mampumembuktikan bahwa sebenarnya windows mampudimaksimalkan.

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DAFTAR PUSTAKA

Junaidi (2006). Memburu Virus RontokBro Dan VariannyaDalam Membasmi Dan Mencegah. Cyber Raharja, 5(3), 82-99.

(2008). Rekayasa Teknik Pemrograman Pencegahan DanPerlindungan Dari Virus Lokal Menggunakan API VisualBasic. CCIT, 1(2), 134-153.

(2008). Teknik Membongkar Pertahanan Virus LokalMenggunakan Visual Basic Script dan Text Editor UntukPencegahan. CCIT, 1(2), 173-187.

Rahmat Putra (2006). Innovative Source Code Visual Ba-sic, Jakarta: Dian Rakyat.

Slebold, Dianne (2001). Visual Basic Developer Guide toSQL Server. Jakarta: Elex Media Komputindo.

Stallings, William (1999), Cryptography and Network Se-curity. Second Edition. New Jersey: Prentice-Hall.Inc

Tri Amperiyanto (2002). Bermain-main dengan VirusMacro. Jakarta: Elex Media Komputindo.

(2004). Bermain-main dengan Registry Windows. Jakarta:Elex Media Komputindo.Wardana (2007). Membuat 5 Program Dahsyat di VisualBasic 2005. Jakarta : Elex Media Komputindo.

Wiryanto Dewobroto (2003). Aplikasi Sains dan Teknikdengan Visual Basic 6.0. Jakarta: Elex Media Komputindo.

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Paper

Computer Accountancy DepartementAMIK RAHARJA [email protected] Informatioan Engineering Departement STMIK RAHARJA

TANGERANG [email protected]

ABSTRACTIT World, which is always up to date at any time and have the innovative world of education, so that the informationcirculating and the more complex. One of the most important aspects in the world of education in a university is makinga fast, precise, accurate and sparingly. No organization or group that released from the performance measurements. Withthe increasing demand of quality education in particular, both in terms of discipline Staff, Lecturers and Students.Academic Universities in Raharja need a method that can answer all the needs in the form of information that’s fast,accurate and appropriate and in decision-making, where information is fast, precise, accurate and sparing is one of theCritical Success Factor (CSF) from an institution education. This study, entitled “Measuring Delone and Mclean Modelof Information System Effectiveness Academic Performance.” Discuss the effectiveness of information systems that canmonitor the performance of each part such as the performance studies program Kapala, Performance and LecturersStudents active, this data can be detected from the user’s perception and behavior in use in Universities Raharja. Thisstudy aims to determine the factors that affect the information received or not the effectiveness of academic performanceby the user. Also want to know the relationship between factors that influence the effectiveness of information systemsacceptance Academic. Model used with the DeLone and McLean model. Information System is expected to serve in thefunction effectively. This shows that the effectiveness of the development of information systems success. The success ofInformation Systems marked with the satisfaction by the user (user Satisfaction), but this success will not mean muchwhen we apply the system can not improve the performance of individuals and Organization. Statistical test performedwith Structural Equation Modeling (SEM)

Keywords: Effectiveness, Critical Success Factor, user satisfaction

Saturday, August 8, 200916:00 - 16:20 Room AULA

Measure Delone and Mclean Model of Information System EffectivenessAcademic Performance

Padeli, Sugeng Santoso

IntroductionE-commerce and Internet are the two main components inthe era of digital economy. Internet as a backbone andenabler of e-commerce which is a collection of processesand business models are based on the network. Both growrapidly extraordinary, mutually related, and affect a varietyof organizations in a way that is very diverse [BIDGOLI2004].Recently, IT has been a dramatic race in both capabilityand affordability, and recognized ability to process data tocapture, store, process, retrieve, and communicate knowl-edge. Thus, many development organizations that is de-signed specifically to facilitate knowledge management.

The goal of the effectiveness Inforasi academic system isto display the information that is useful for others as theuser ratings or grip objects in the decision-making, Perfor-mance management systems effectiveness Inforasi are pri-marily academic efforts to re-otomatisasikan academic per-formance management process through the installation ofsoftware designed specifically for it. Paramternya calleddahsboard although still using the shape and color be-cause it looks so simple in design quickly identify the userby the position of each section that are either on or posisweak performance.

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Performance management process management educationis not rare to disappear in our administrative process of themany piles of paper. Imagine how many pieces of paperthat must be when we have to print and managememantantau monthly performance of each division or takea policy when the lectures a semester is complete. At thetime of evaluating the lectures in our day Raharja usuallyrequire a long time because they had to analyze the datathat is input in the input .. Automation through the perfor-mance dashboard akan men-streamlined-kan keribetan allthat. The process of becoming much more efficient, withpiles of paper that pile up every corner of the very tablewe.Successful or not an information technology system in theorganization depends on several factors. Based on thetheories and the results of previous research that has ex-amined, DeLone and McLean (1992) and then develop amodel of a complete but simple (Parsimoni) which they callby the name of the model of success DeLone and McLean(D & M IS Success Model).

Analysis Problems.After analyzing the facts of the development of informa-tion technology especially in the field of Information Sys-tems, the authors identify the technologies that are ap-plied and the matter of preference as a problem this thesisresearch.Model proposed to test this model of success DeLone andMcLean (D & M IS Success Model) mereflekiskan depen-dence six measurements of information system success.The six elements or factors are:1. Quality system (system quality)2. Information quality (information quality)3. Use (use)4. User satisfaction (user satisfaction)5. Individual satisfaction (individual impact)6. The impact of the organization (organization impact)

In this research the problem will be examined are:1. These factors are significantly contributing to the effec-tiveness of information systems in the academic perfor-mance of university Raharja.2. How big a model in ujikan in this research provides anexplanation of the effectiveness of the academic level.

DiscussionAspects of behavior (behavior) in the Acceptance of In-formation Technology([Syria 1999], 17) the use of Information Technology (IT)for the company is determined by many factors, one ofwhich is characteristic of IT users. Differences in the char-acteristics of IT users are also influenced by aspects of theperceptions, attitudes and behavior in the use of IT toreceive. A user’s system is that human behavior has a psy-

chological (behavior) that have been on himself, whichcaused the aspect of user behavior in an information tech-nology becomes an important factor in each person usinginformation technology.

As the size of the systemAccording to DeLone and McLean, many researchers havebeen using as a measure of the success of the systemobjectives. The impact caused the system is used if thesystem should be very useful and successful ([Seddon1994], 92). If the use of forced, then the frequency of use ofthe system and the information presented will be reducedso that success is not achieved ([Seddon 1994], 93).Benefits of an information system is the level where a per-son believes that using the system thoroughly to improvethe performance ([Seddon 1994], 93).Use of focus on actual use, the widespread use in thework, and many information systems used in the job([Almutairi 2005], 114). Usage is defined as a result of inter-action with the user information ([DeLone 1992], 62).Information System Success Model DeLone and McLeanMeasuring the success or effectiveness of informationsystems is essential for our understanding of the valueand power of action and investment management informa-tion system. DeLone and McLean stated that (1) the qual-ity of the system to measure the success of the technique,(2) the quality of information to measure the success ofthe SI, and (3) the use of the system, user satisfaction,individual impact and organizational impact measures theeffectiveness of success. Shannon-Weaver stated that (1)level of technical communication as a communication sys-tem that the information accurately and efficiently, (2) thelevel of success is the SI information in the SI is the mean-ing; and (3) the level of effectiveness is the influence ofinformation on the recipient / user ([DeLone 2003], 10).Based on the statement DeLone-McLean and Shannon-Weaver mentioned above, it can be concluded that 6 (six)the success of inter-related dimensions. Model is a pro-cess information system and made the first of several fea-tures that can be grouped into several levels of qualityand information systems. Next, the user use the featuresto the system where they are satisfied or not satisfied bythe system or the resulting information. Results and infor-mation systems used to provide impact or influence toeach individual to the behavior of their work, and this groupis to provide impact or influence on the organization([DeLone 2003], 11). As an illustration this description canbe seen in Figure 2.1.

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Figure 2-1 Information Systems Success Model DeLoneand McLean

Quality system and quality of information in the Informa-tion System Success Model DeLone and McLean, on itsown or together to give effect to the use and satisfactionof users ([Livari 2005], 9). This indicates that the use andsatisfaction of the user related, and directly affect the indi-vidual, which ultimately affect the organization. DeLoneand McLean states that the characteristics of the system’squality characteristics as obtained from the informationsystem itself, and as a characteristic quality of the infor-mation obtained from the results of information ([Livari2005], 9).

DeLone and McLean inform characteristics that impactthe individual as an indication that the information systemhas provided a better understanding to the user informa-tion about the decision to increase the productivity ofindividual decision making, provide a change of user ac-tivity, and change the perception of decision makersabout the benefits and importance of system information([Livari 2005], 9).

Display ScreenViews effectiveness of information systems academic per-formance In the prototype image in the 2.1 display theeffectiveness of information system on the universitywebsite can Highest Raharja Department Head taking ac-tivities, active students in lecture, a lecturer in the activefill the class. Head of Department can also oversees activestudents, improving the quality of lecturers, teaching learn-ing process (PBM).

pleteness of teaching materials that have been providedin each meeting.

Development of Flow Diagram (path diagram)After the theoretical model was built, and described a pathdiagram. Usually known that the causal relations expressedin the form of equality. But in the SEM (in operation Amos)kausalitas relationship is depicted in a path diagram. Fur-thermore, the language program will convert the image tobe equality, and equality to be estimated.Destination dibuatnya path diagram is to facilitate research-ers in view kausalitas relationships who want to test. Rela-tionships between konstruk stated the shaft. Arrow thatpoints from a konstruk to konstruk others show causalrelationships.On this research, the path diagram is constructed as shownin Figure III-1 below:

Figure 2-2, The diagram in the Research Model

Conversion to the flow diagram in equation After steps 1and 2 is done, researchers can begin to convert into amodel specification of a series, which are:

Equality-equality Structural (Structural Equations)Equality is formulated to reveal the relationship betweenkausalitas different konstruk, formed with the latent vari-ables measurement model eksogenous and endogenous,persamaannya form of:

P = ã ã11KS + 22KI + â21KP + ò1 (1)KP = ã21KS + 22KI + ã â11P + ò2 (2)DI = â21P + â22KP + ò3 (3)

Equality of measurement model specification (Measure-ment Model)Researchers determine which variables to measure konstrukwhich, as well as a series of matrix showing the correlationbetween the dihipotesakan or konstruk variables. The formof indicators of latent variables and indicators eksogenous

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latent endogenous variables are:Measurement of the indicator variable eksogenous

X1 = ë11KS + ä1X2 = ë21KS + ä2X3 = ë31KS + ä3X4 = ë41KS + ä4X5 = ë51KS + ä5X6 = ë61KI + ä6X7 = ë71KI + ä7X8 = ë81KI + ä8X9 = ë91KI + ä9

Measurement of endogenous indicator variablesy1 = ë11P + å1y2 = ë21P + å2y3 = ë31P + å3y4 = ë41KP + å4y5 = ë51KP + å5y6 = ë62KP + å6y7 = ë72DI + å7y8 = ë82DI + å8y9 = ë93DI + å9

An Empirical Research on the Application of the DeLoneand McLean Model in the Kuwaiti Private Sector, foundthat (1) of 29% quality of the information calculated by thequality of the system. Calculation of F test is significantwhere the value of F = 38.36 on the level of alpha <0.01.Positive beta value of 0:53, indicating the quality informa-tion has a significant positive influence on the quality ofthe system. So that the statistical calculation can be saidthat the relationship between quality and quality of infor-mation systems is positive. (2) the quality of informationand quality system significantly affect user satisfaction.43% of the two variables is calculated by the user satisfac-tion. Calculation of F test is significant where the value ofF = 34.98 on the level of alpha <0.01. Positive beta value of0:41 for the quality of information and positive beta valueof 0:31 for the quality system. This indicates that thesetwo variables have a significant positive influence on usersatisfaction at the level p <0.01. (3) equal to 9% by the useof SI is the impact of the individual. Calculation of F test issignificant where the value of F = 9.90 at alpha <0.01. Posi-tive beta value of 0:32, indicating that the SI has a signifi-cant positive impact on individuals ([Almutairi 2005], 116).

ConclusionBased on the results of research and interpretation, theconclusion can be drawn as follows. Testing the relation-ship between quality and satisfaction to the user’s systemto provide results that the quality system and the relation-ship has a significant influence on user satisfaction. So,when the system improved the quality of the user satisfac-tion will also increase. Testing the relationship between

quality and impact of information on user satisfaction re-sults that provide quality information and have a relation-ship significant to the satisfaction of users. So, when thequality of information improved the user satisfaction willalso increase. Testing the relationship between the useand influence of user satisfaction and vice versa, to pro-vide results that have a relationship and a significant in-fluence on user satisfaction as well as vice versa. Boththese variables affect each other, so that when one vari-able is increasing the other variable will also increase. Test-ing the relationship between the use and influence to theimpact of individual results that the use and effect rela-tionship has a significant impact on the individual. So,when the impact of increased use of the individual willalso be increased. Testing the relationship between satis-faction and the influence of the user to the impact of indi-vidual returns that user satisfaction and the relationshiphas a significant influence on individual impact. So, im-proved user satisfaction when the individual will also im-pact increased.

REFERENCES

[Almutairi 2005] Almutairi, Helail, “An Empirical Applica-tion of the DeLone and McLean Model in the KuwaitiPrivate Sector”, Journal of Computer Information Systems,ProQuest Computing, 2005.

[Banker 1998] Banker, Rajiv D., et.al., “Software Develop-ment Practices, Software Complexity and Software Main-tenance Performance: A Field Study”, Journal of Manage-ment Science, ABI / Inform Global, 1998.

[DeLone 1992] DeLone, William H. and Ephraim R. McLean,“Information Systems Success: The Quest for dependentVariable”, Journal of Information Systems Research, TheInstitute of Management Sciences, 1992.

[DeLone 2003] DeLone, William H. and Ephraim R. McLean,“The DeLone and McLean Model of Information SystemsSuccess: A Ten-Year Update”, Journal of Management In-formation Systems, ME Sharpe Inc., 2003.

[Doll 1994] Doll, William J., et.al., “A Confirmatory FactorAnalysis of the End-User Computing Satisfaction Instru-ment,” MIS Quarterly, University of Minnesota, 1994.

[Ghozali 2004] Imam Ghozali, “Structural Equation Model,Theory, Concepts and Applications with the Program Lisrel8:54”, Publisher Undip, Semarang, 2004.

[Goodhue 1995] Goodhue, Dale L. and Ronald L. Thomp-son, “Task-Technology Fit and Individual Performance”,MIS Quarterly, University of Minnesota, 1995.

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[Haavelmo 1944] Haavelmo, T., The Probability Approachin Econometrica. Econometrica, 1944.

[1998 Hair] Hair, J. F., Multivariat Data Analysis, New Jer-sey, Prentice Hall, 1998.[Hamilton 1981] Hamilton, and Norman L. Scott Chervany,“Evaluating Information System Effectiveness - Part 1:Comparing Evaluation Approaches,” MIS Quarterly, Uni-versity of Minnesota, 1981.

[Hayes 2002] Hayes, Mary, “Quality First”, InformationWeek, 2002. [Ishman 1996] Ishman, Michael D., “Information measur-ing Success at the Individual Level in Cross-Cultural Envi-ronments”, Information Resources Management Journal,ABI / Inform Global, 1996.

[Jerry81] Jerry Ardra F. Fitzgerald Fitzgerald and Warren D.Staliings, Jr.., Fundamentals of system analys (second edi-tion, New York: Jhon Willey & Sens, 1981)[Joreskog 1967] Joreskog, K. G., Some Contribution toMaximum Likelihood Factor Analysis, Psychometrika, 1967.[Kim 1988] Kim, Chai and Stu Westin, “Software Maintain-ability: Perceptions of EDP Professionals,” MIS Quarterly,ABI / Inform Global, 1988.

[Lee 1995] Lee, Sang M., et.al., “An Empirical Study of theRelationships among End-User Information Systems Ac-ceptance, Training, and Effectiveness”, Journal of Man-agement Information Systems, ABI / Inform Global, 1995.[Lin 2004] Lin, Fei Hui and Jen Her Wu, “An EmpiricalStudy of End-User Computing Acceptance Factors in Smalland Medium Enterprises in Taiwan: Analyzed by Struc-tural Equation Modeling”, The Journal of Computer Infor-mation Systems, ABI / Inform Global , 2004.

[Livari 2005] Livari, Juhani, “An Empirical Test of theDeLone-McLean Model of Information System Success,”The Database for Advances in Information Systems,ProQuest Computing, 2005.

[Lucas 1975] Lucas, Henry C.Jr., “Performance and the Useof an Information System”, Journal of Management Sci-ence, Application Series, 1975.

[Ngo 2002] Ngo, David Chek Ling, et.al., “Evaluating In-terface Esthetics,” Journal of Knowledge and InformationSystems, Verlag London Ltd.., 2002.

[Nur 2000] Nur Indriantoro, Computer Anxiety of Influ-ence Skills Lecturer In Use of Computers, Journal Account-ing and Auditing (JAAI) Vol.3 No.1, FE UII, Yogyakarta,2000.

[O’Brian 2003] James A. O’Brien, “Introduction to Infor-mation System”, Eleventh Edition, Mc Graw Hill, 2003[O’Brien 2005] O’Brien, James A., Introduction to Informa-tion Systems, 12th ed., McGraw-Hill, New York, 2005.[Pasternack 1998] Pasternack, Andrew, “Hung Up on Re-sponse Time”, Journal of Hospitals & Health Networks,ABI / Inform Global, 1998.

[Rai 2002] Rai, Arun, et.al., “Assessing the Validity of ISSuccess Models: An Empirical Test and Theoritical Analy-sis”, Journal of Information Systems Research, ProQuestComputing, 2002.

[Tonymx 60] F. Neuschen Management, by system, (sec-ond edition, New York: McGrawhill, 1960).

[Riduwan 2004] Riduwan, Method & Technique Develop-ing Thesis, First Printed, Alfabeta, Bandung, 2004.

[Sandjaja 2006] Sandjaja, B, and Albertus Heriyanto, Re-search Guide, Reader Achievements, Jakarta, 2006.

[Satzinger 1998] Satzinger, John W. and Lorne Olfman,“User Interface Consistency Across End-User Applica-tions: The Effects on Mental Models”, Journal of Man-agement Information Systems, ABI / Inform Global, 1998.[Seddon 1994] Seddon, Peter B. and Min Yen Kiew, “APartial Test and Development of DeLone and McLean’sModel of IS Success”, University of Melbourne, 1994.

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Computer Science Faculty of Sriwijaya University Palembang-Indonesia.E-mail: [email protected]

AbstractSoftware development is a complex task and requires adaptation to accommodate the needs of the user. To make it easierto changes the software, in maintenance, now has developed concept in the development of the software, the model-view-controller pattern, which is the architecture that can help facilitate in the development and maintenance of soft-ware, because in this architecture for a three-layer model, namely, the view and controller in development done indepen-dently, so that it can provide convenience in the development and maintenance. In addition, this architecture can alsoview a simple and interesting for the user. Software system on-line test is software that requires interaction with the user,and maintenance of adaptive software. Because the test system on-line requires the development of software to accommo-date the needs of this growing quickly. This paper to analyse the Model-View-Controller and try development, to applyit in the development of software system test on-line.

Keywords: Model-view controller, architecture, pattern on-line test

Saturday, August 8, 200916:00 - 16:20 Room L-210

The Concept of Model-View-Controler (MVC) as Solution Software Development(case study on the development of solutions Software on-line test)

I. INTRODUCTION

1.1.Background Software development is a complex task. Softwaredevelopment process, from concept to implementation,known by the term System Development Life Cycle (SDLC)which includes stages such as requirement analysis,design, code generation, implementation, testing andmaintenance (Pressman, 2002; 37-38). In the softwaredevelopment also requires architecture or pattern that canhelp in the development of software. Currently, many software development utilizing aconcept of programming by using the Model-view-controller pattern, which is the architecture with a lot ofhelp in the development of software that is easy tomaintenance, especially those based interaction with theGUI (Graphical User Interface) and the web. This is inaccordance with the statement Ballangan (2007) : “MVCpattern is one of the common architecture especially inthe development of rich user interactions GUI Application.Its main idea is to decouple the model. The interactionbetween the view and the model is managed by thecontroller. “ In addition Boedy, B (2008) [1] states that: MVC is aprogramming concept that applied to many of late. By

applying the MVC to build an application will be impactto ease at the time the application enters the maintenancephase. Development process and integration is becomingeasier to do. Basic idea of MVC is actually very simple,namely to try to separate model, view, and controller.The concept of software development with thearchitectural model, controller and view this is very helpfulin the development of software test on-line. This is becausesoftware development with the concept of model viewcontroller, and this can help make it easier to domaintenance and make the look interesting, so that userinteraction with the software more attractive and easier. This study focused on the concept of software-basedGUI to apply the Model-View-Controller pattern is appliedto the development of software Testing On-line at theComputer Science Faculty of Sriwijaya Universityconsiderations related to the concept in terms of quality,flexibility and ease of maintenance (expansion orimprovement), as cited by many of the literature. From the background that has been described abovewill be conducted the research with the title: “The conceptof Model-View-Controler (MVC) in the case studysolutions Software development test on-line”

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1.2. Problem Formulation Research was conducted in order to solve theproblem how architecture Model-View-Controller (MVC)in the ease of making the software, in this case studyapplication to take the exam online.1.3 Objectives and Benefits

1.3.1 Research Objectives This study aims to understand the development

of Software architecture model with the pattern model-view-controller with on a case study to implement thesoftware system on-line test.1.3.2 Benefit Research Benefits obtained with this research are:1.Understanding the concept of MVC approach in thedevelopment of software test on-line.1.4 Research Method

Steps will be done in this research are:1. Study of literature on the concept of architectural

pattern Model-View-Controller.2. Implementing MVC architectural pattern in

development software test on-line.

II. OVERVIEW REFERENCES

2.1Definition Popadyn, 2008 [8] defines: Model - View - Controlleris an Architectural pattern used in software engineering.Nowadays such complex patterns are Gaining more andmore popularity. The reason is simple: the user interfacesare becoming more and more complex and a good way offighting against this complexity is using Architecturalpatterns, such as MVC.MVC pattern consists of three layers as in research byPassetti (2006): The pattern separates Responsibilitiesacross the three components, each one has only oneresponsibility.• The view is responsible only for rendering the UIelements. It gives you a presentation of the model. Inmost implementations the view usually gets the state andthe data it needs to display directly from the model.• The controller is responsible for interacting between theview and the model. It takes the user input and figures outwhat it means to the model.• The model is responsible for business behaviors,application logic and state management. It provides aninterface to manipulate and retrieve its state and it cansend notifications of state changes to observers.Each layer has the responsibility of each of each integratedwith one another. The MVC abstraction can be graphicallyrepresented as follows.

Figure 1. MVC abstraction

Events typically cause a controller to change a model,or view, or both. Whenever a controller changes a model’sdata or properties, all dependent views are automaticallyupdated. Similarly, whenever a controller changes a view,for example, by revealing areas that were previouslyhidden, the view gets data from the underlying model torefresh itself.

2.2 Common Workflow

The common workflow of the pattern is shown on thenext diagram.

Figure 2. Common workflow MVC pattern

Control flow generally works as follows:

· The user interacts with the user interface in someway(e.g. presses buttons, enters some input information,etc.).

· A controller handles the input event from the user interface.

· The controller notifies the model of the user action,possibly resulting in a change in the model state

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· A view uses the model to generate an appropriate userinterface. The view gets its own data from the model.The model has no direct knowledge of the view.

· The user interface waits for further user interactions,which then start a new cycle.

2.3 Model-View-Controller Architecture

On the development of software architecture withthe pattern or model-view-controller, the softwaredevelopment divide into three parts, namely the model,view and controller.

Biyan (2002) points out: MVC is a design pattern thatenforces the Separation between the input, processing,and output of an application. To this end, an applicationis divided into three core components: the model, the view,and the controller. Each of these components handles adiscreet set of tasks.

a. Model

Model here as a representation of the data involvedin a transaction process. Each time the method / functionof an application need to access to data in a, the function/ method is not directly interaction with the source datathrough the model but should be first. In this model onlyallowed to interact directly with the source data.

b. View

View as the presentation layer or user interface(display) for the user of an application. Data needed bythe user will be formatted in such a way that can be runand presented with the view that the format is adjustedto the user requirement.

c. Controller

Controller is logic aspect of an application. Controllerwill determine the process bussiness of applications arebuilt. Controller will respond to each user’s input with theconduct of the model and view so that the appropriaterequest / demand from the user can be met well.

Each layer are interconnected and mutual dependenceof each other, this is like that disclosed by Anderson, DJ(2000), As we are building each View on demand, it mustrequest data from the model every time it is instantiated.Hence, there is no notification Model to View. The creationof the View object which must demand any necessarydata from the model.

Views and Controllers together can be considered thePresentation Layer in a Web Application. However, as we

will see it is easy to cleanly separate Views from Controllersin a Server-side Web Application.

Figure 3. Client to Presentation Layer Interactionshowing the MVC Separation at the Server

The Model, on the other hand, is separate from thePresentation Layer Views and Controllers. It is there toprovide Problem Domain services to the PresentationLayer including access to Persistent Storage. Both Viewand Controller may message down to the model and it inturn will send back appropriate responses.

Some of the MVC in field research has been done,among others:Sauer in the research entitled “MVC-Based ModelingSupport for Embedded Real-Time Systems” states that“Effects of real-time requirements on the model, view,controller and communication components have to beidentified, eg in the scenario for signalling to a warningmessage and handling it by the user or in the case ofexception handling. It has to be answered, which timeconstraints apply to each component “effects in real-timerequirement on the model view controller can be identifiedso that it can be signed quickly. Ogata in their paper describes the design of theframework that the Model-View- Controller is called Event-driven MVC-Active is based on the active object. On thismodel treats each input by using the Event-drivenmechanism and the process of placing objects in the activedisplay. This model is very suitable for applications thatfocus on the GUI. This model has been implemented tothe Smalltalk. Naturo, et all, (2004) propose a design pattern thatsupports the construction of adaptable simulationsoftware via an extension of the Model / View / Controldesign pattern. The resulting Model / Simulator / View /Control pattern incorporates key concepts from the DEVSmodeling and simulation methodology in order to promotea Separation of modeling, simulation, and distributedcomputing issues. The advantage of this simulationapproach to software design is considered in the context

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of other documented attempts to promote componentbased simulation development [6]. Veit (2003) propose to use the model-view-controllerparadigm as a benchmark for AOSD approaches, since itcombines a set of typical problems concerning theSeparation and integration of contens. [11] Morse, SF et all(2004) introduce Model-View-Controller Java Application Framework. This paperpropose a simple application framework in Java thatfollows a Model-View-Controller design and that can beused in introductory and core courses to introduceelements of software engineering . Although it is focusedon applications having a graphical interface, it may bemodified to support command-line programs. Theapplication framework is presented in the context ofdevelopment tools Apache Ant and JUnit. [5] Software development through the MVC approach,maintenance and evolution of the system more easy todo, with the divide in layers. The layers in the softwaredivide into three layer.By applying the concept of MVC, the source code of anapplication to be more tidy and easier to maintenance anddeveloped. In addition, the program code has been written thatcan be used again for other applications by changing onlysome. Basically, the MVC to separate the data processorreferred to as a model and user interface (UI) or HTMLtemplate in this case called the View. Mean while, as thefastener Controller Model “View to handle the request sothat the user data is displayed correctly.2.4 Online Testing Testing activities as one of the activities in theteaching-learning can be done anywhere with the helpof information technology. This activity is called withthe test on-line. In the test conducted on-line, the need for themany variations of problems and supply problems arevery important addition to the maintenance of thesoftware is needed to accommodate the needs ofadaptation to the development of the user.Effectiveness and level of security required in bothdocuments the shoptalk addition, the tests alsorequired a good appearance and reliable in showingshoptalk test. Is required for a concept developmentand architecture in both software development supporton-line test this.

III. RESULTS AND DISCUSSION

3.1. Analysis of Needs The results of the analysis needs in the form ofFunctional Requirement have 3 main menus: the loginprocess, the main page, the lecturers and the studentMenu

3.2. MVC Architecture Concept Analysis Software Testing Online Architecture Model-View-Controller pattern is thatcan help build the project more effectively. In thedevelopment of this pattern is done with dividecomponent of Model, View and Controller in thedevelopment of software. Divide is useful to separatethe parts in the application so that the ease inapplication development and maintenance. This is inline with the expression: Model-View-Controller designsoftware to assist with the needs divide similar. ModelView Controller is a class analysis to the stages of thedesign phase. [7] On a system equipped with thesoftware, the changes are often the user interface. Userinterface is the part that dealt directly with the user andhow it interacts with the application, the focus pointmade to conversion based on ease of use.Business-logic in a complex user-interface to makeconversion to the user interface became more complexand simple mistakes occur. Changes one part has thepotential of the overall software.MVC can provide a solution to the problem by dividingthe pattern into sections separate, Model, View andController, split between the part and create a system ofinteraction between them.

Figure 5. MVC architecture is applied to the softwaretest online

3.2.1. Model layer Layer model in the MVC pattern that represents thedata used by applications as business processesassociated it.Domain Model is a representation of the model layer.Whole class at the model are in a layer model, includingthe class that supports it (DAO class).3.2.2. View layer This layer contains the entire details of theimplementation of the user interface. Here, thecomponents provide a graphical representation internalapplication process flow and lead to the applicationuser interaction. There is no other layer that interactswith the user, only the View.On the software Online Testing System which is beingdeveloped, a layer view of the files containing thedisplay for the user that consists mainly of HTMLscript.

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3.3.3. Controller layer Controller layer in MVC provides detailed programflow of each layer and transition, will be responsible forgathering events made by the user from the View andupdate components of the model using data entered bythe user. On the software Online Testing System which is beingdeveloped, there is only one controller class that is FrontController class. Use one Controller class is because thescope of the software that is still small, so the controllerdoes not need separation.

IV. CONCLUSIONResults from this research are:

1. Development of software can be divide in severallayers.

2. Divide layer in the development of softwarearchitecture with the concept of Model-View-Controller can assist in system maintenance andevolution

3. Software development Testing systemarchitecture apply online with the Model-Controller-View very helpful in furtherdevelopment, because the software system testonline this very need for reliability Usersinterface.

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[3] Hariyanto, B, 2004., “Object-oriented SystemsEngineering”, Bandung: Information Cackle, B, 2002,the MVC design pattern brings about betterorganization and code reuse, Oct 30, 2002 8:00:00 AM

[4] Mathiassen, L, Madsen, Andreas. M, Nielsen, Peter.A, and the Stage, A, 2000, “Object Oriented Analysis& Desig. Issue 1. Forlaget Marko, Denmark.

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[6] Nutaro, A and Hammonds, P, 2004, combining the

Model / View / Control Design Pattern with the DEVSFormalism to achieve Rigor and Reusability inDistributed Simulation, JDMS, Vol. 1, Issue 1, April2004 Page 19-28, © 2004 The Society for Modelingand Simulation International

[7] Nugroho. A, 2002, “Analysis and Design Objectoriented System,” Bandung: Information. Passeti,2006, The MVC Pattern © 2006, P & P Softwarewww.pnp software.comhttp: / / images.google.co.id /imgres? Imgurl = http://www.pnp-software.comeodisp/images/mvc -original.

[8] Popadiyn, P, “Exploring the Model - View - Controller(MVC) pattern,” Pencho Popadiyn posted by on Dec17, 2008

[9] Pressman, RS, 2002, “Software Engineering:Practitioners Approach” Translated by: CN.Harnaningrum (Book 1). Yogyakarta: ANDI. Sutabri,T, 2004, “Analysis of Information Systems”,Yogyakarta: ANDI.

[10] O’Brien, James A. (2003). Introduction to informationsystems: Essentials for the e-business enterprise,edition to-11. McGraw-Hill, Boston.

[11] Veit, and M Herrmann, S, 2003, “Model View Controllerand Object Teams: A Perfect Match of Paradigms”,avalaibale on http://citeseerx.ist.psu.edu/viewdocsummary?doi=10.1.1.12.5963

[12] Model-View-Controller Pattern, Copyright © 2002eNode, Inc.. All Rights Reserved.http: / /www.enode.com / x / markup / tutorial / mvc.html

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j2ee/MVCPatternAndFrameworks_speakernoted.pdf+ MVC + pattern & hl = en & ct = clnk & cd = 14 & gl= id

[14] ——, 2006, MVC Pattern (MVC Framework) http:/w w w . j a v a p a s s i o n . c o m / j 2 e eMVCPatternAndFrameworks_speakernoted.pdf

[15] http://www.mercubuana.ac.id/sistem.php, access in 4september 2008 http://id.wikipedia.org/wikiPerangkat_lunak, “software”, tgl.4 access inSeptember 2007.

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Abstract.In this paper, we propose an identification method of the land cover from remote sensing data with combining neuro-fuzzyand expert system. This combining then is called by Neuro-Fuzzy Expert System Model (NFES-Model). A Neural network(NN) is a part from neuro-fuzzy has the ability to recognize complex patterns, and classifies them into many desiredclasses. However, the neural network might produce misclassification. By adding fuzzy expert system into NN usinggeographic knowledge based, then misclassification can be decreased, with the result that improvement of classificationresult, compared with a neural network approximation. An image data classification result may be obtained the secretinformation with the inserted by steganography method and other encryption. For the known of secret information, weuse a fast fourier transform method to detection of existence of that information by signal analyzing technique.

Keywords: steganography, knowledge-based, neuro-fuzzy, expert system, signal analyzing.

Saturday, August 8, 200913:55 - 14:15 Room AULA

Signal Checking of Stegano Inserted on Image Data Classification by

NFES-Model

1. Introduction

Neuro-fuzzy expert system model (NFES – Model) can bedivided into two sub-systems which consist of neuro-fuzzysystem and expert system. Neuro-fuzzy system is a combi-nation of neural networks and fuzzy systems, where eachhas independent areas. The connections to each other aremerely marginal but both bring benefit for the solution ofmany problems.

Lotfi A. Zadeh introduced the concept of fuzzy sets in1965. In 1974, E.H. Mamdani invented a fuzzy inferenceprocedure, thus setting the stage for initial developmentand proliferation of fuzzy system applications. Logic pro-gramming also played an important role in disseminatingthe idea of fuzzy inference, as it emphasizes the impor-tance of non-numerical knowledge over traditional math-ematical models [4].

Expert systems are computer programs which use sym-bolic knowledge to simulate the behavior of human ex-perts, and they are a topical issue in the field of artificialintelligence (AI). However, people working in the field ofAI continue to be confused about what AI really is pro-posed by Schank [6]. In other words, there are attempts toconf properties (or attributes) to a computer system underthe guise of AI, but the practitioners find difficulty in de-fining these properties! It is generally accepted that anexpert system is useful when it reaches the same conclu-sion as an expert [7].

The most recent wave of fuzzy expert system technologyuses consolidated hybrid architectures, what we call Syn-ergetic AI. These architectures developed in response tothe limitations of previous large-scale fuzzy expert sys-tems.

193

M. Givi EfgiviaStaf Pengajar STMIK Muhammadiyah Jakarta

Safaruddin A. PrasadStaf Pengajar Fisika, FMIPA, UNHAS, Makassar

Al-Bahra L.B.Staf Pengajar STMIK Raharja, Tangerang

E-mail : [email protected], [email protected] : [email protected]

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The NFES-Model is developed and implemented to ana-lyze of land cover classification on the field of Maros Dis-trict on South Sulawesi Province, Indonesia.

The fuzzy logic is used to analyze of remote sensed datafor land cover classification since Maros District is com-plex geography, the remotely sensed image has variousgeometrical distortions caused by an effect the complexearth surface, such as the shadow of hills.

Remotely sensed image data sampled from a satellite in-cludes specific problems such as large image data size,difficulty in extracting characteristics of image data and aquantity of complex geographical information in a pixeldue to its size of 30 m2. In the past, we have used a statis-tical method such as a maximum likelihood method withoutconsidering these problems. The maximum likelihoodmethod identifies a recognition structure by a statisticalmethod using the reciprocal relation of density value dis-tribution per one category. This method is based on anassumption that image data follows the Gaussian distribu-tion.

An image data classification may be inserted by the secretinformation for intelligence requires or information hidingart on the image data by the steganography method. Now,if a governance institute to obtain the image data received,then to appear ask, what that image data is contain thesecret information?

Signal of an image data classification or other image datacan be checked by using a fast Fourier transform algo-rithm. This checking the main for uncovering the secretscreen is entered in to image data received.

2. Neuro-Fuzzy Expert System (NFES)

2.1. Architecture of NFES-ModelFigure 1 showed NFES-Model architecture as neural net-work (NN) architecture with four hidden layers, one inputlayer, and one output layer. In this NFES-Model architec-ture showed parallel structure and data followed in themodel, respectively for learning (backward path) and clas-sification (forward path). It is in the image data processingwill be improved upon classification result and the imageclassification can be visualized.

Each layer in the NFES-Model (figure 1) is associated bycertain stage in the fuzzy inference processing. In com-pletely, each layer is explanation contain as follows:Layer-1 : The input layer. Each neuron in this layer trans-mits external crisp signals directly to the next layer. That is,

)1()1(ii xy =

Layer-2 : The fuzzification layer. Neurons in this layer rep-resent fuzzy sets used in the antecedents of fuzzy rules. Afuzzification neuron receives a crisp input and determinesthe degree to which this input belongs to the neuron’sfuzzy set. The activation function of a membership neuronis set to the function that specifies the neuron’s fuzzy set.We use triangular sets, and therefore, the activation func-tions for the neurons in layer-2 are set to the triangularmembership functions. A triangular membership functioncan be specified by two parameters {a, b} as follows:

Figure-1. The architecture of NFES-Model

Layer-3 : The fuzzy rule layer. Each neuron in this layercorresponds to a single fuzzy rule. A fuzzy rule neuronreceives inputs from the fuzzification neurons that repre-sent fuzzy sets in the rule antecedents. For instance, neu-ron R1, which corresponds to Rule-1, receives inputs fromneurons PR1, PR2, and PR3.

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In a neuro-fuzzy system, intersection can be imple-mented by the product operator. Thus, the output of neu-ron I in layer-3 is obtained as:

)3()3(2

)3(1

)3( ... kiiii xxxy ×××=

1321)3(

1 RPRPRPRRy µµµµ =××=Layer-4 : The output membership layer. Neurons in thislayer represent fuzzy sets used in the consequent of fuzzyrules.Each output membership neuron combined all its inputsby using the fuzzy operation union. This operation can beimplemented by the probabilistic OR (⊕ ). That is,

)4()4(2

)4(1

)4( ... kiiii xxxy ⊕⊕⊕=

1876432)4(

ORRRRRROiy µµµµµµµ =⊕⊕⊕⊕⊕=Layer-5 : The defuzzification layer. Each neuron in this layerrepresents a single output of the neuro-fuzzy expert sys-tem. All neurons in layer-4 are combines them in to a unionoperation for product operation results, and it is called asum-product composition.

772211

777222111

......

OOOOOO

OOOOOOOOO

bbbbababay

×⊕⊕×⊕×××⊕⊕××⊕××

=µµµ

µµµ

The next operation is defuzzification to be inputfor neuron in the next layer. Layer-6 : The output networking. The neuron inthis layer is accumulation of all processing series in NFESnetworking. In the NFES implementation, the neuron inthe layer-6 is appears as classification map.

Each input variables is used on the networks, we mustestablished haw much the fuzzy sets are used for the do-main partition of each variable. By the domain partition foreach variable and linguistic terms, then we can do classifi-cation and we are obtained it classification result [5].

2.2. Algorithm NFESAn algorithm presented of NFES-Model to land cover iden-tification. The NFES algorithm can be written with detailedas follow as:Step-1 : Determine number of m-th membership functionsfor k-th inputsStep-2 : Rule generated for j-th classStep-3 : Make a training and error calculate in j-th class

(

j∈

) with the formula

( )N

Gxk

N

i

ki

ki

j *3

3

1 1

2∑∑= =

−=∈

Which,N = number of pixels in the j-th classx = value of pixel in the classification imageG = value of pixel in the ground-truth image.Then index of three (3) showed the input data is

consist of three channels.Step-4 : IF Îj > Ît THEN return to step-2 (t = tolerance)Step-5 : make the step-4 until km iterationStep-6 : IF Îj > Ît THEN return to step-1

Step-7 : Îj < Ît THEN { }jCxxC ∈= | . This expressionshowed that C is a set which the elements are x such as xis element of j-th class.

3 The Experimental Design

Figure 2 showed the land cover classification procedurescheme of NFES-Model. The image data classification us-ing neuro-fuzzy expert system (NFES) is divided becameof three partition, that is namely, pre-processing by fuzzyc-mean method, pattern recognition by neuro-fuzzy sys-tem, and the checking by knowledge representation.

3.1. Pre-processing by Fuzzy C-MeanClustering implies a grouping of pixels in multispectralspace. Pixels belonging to a particular cluster are thereforespectrally similar. Fuzzy C-Mean (FCM) is a one from group-ing is based Euclidian distance. Prasad [6] group usingFCM algorithms to land cover classification. Such as iscarried out by Sangthongpraow [7] group also.If x1 and x2 are two pixels whose similarity is to be checkedthen the Euclidean distance between them is

),( 21 xxd = 21 xx −

= ( ) ( ){ } 2/1

2121 xxxx t −−

= ( )2/1

1

22

21

⎭⎬⎫

⎩⎨⎧

−∑=

N

iii xx

(1)

Where N is number of spectral components.

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Figure-2. The NFES-Model procedure scheme for landcovers classification

A common clustering criterion or quality indicator is thesum of squared error (SSE) measure, defined as:

SSE = ( ) ( )∑∑∈

−−i iC Cx

it

i MxMx

= ∑∑∈

−i iC Cx

iMx 2

(2)Where Mi is the mean of the i-th cluster and xÎCi is a pat-tern assigned to that cluster [6][7].

3.2. Pattern Recognition by Neuro-Fuzzy SystemIn this part, do it processing in to four steps. The first stepis fuzzification processing of crisp value. The second stepis editing of membership function, include to determiningof number of membership function for each input. Thethirth step is training and testing process. The fourth stepis defuzzification processing for pre-classified requirement.

The next step is checking the pre-classified result. What itoptimal classification or not? If pre-classified result or nextclassified result is not optimal yet, then the up-dated ofknowledge base and then checking classified in loop.Optimalization of classified is doing by seek number ofmisclassification. If the value of misclassification to reachesis desire, then the checking is stopped. Then we obtainedthe final classification. From the final classification result,we are checking it image about the existing of signal noise

or secret information inserted. Signal checking it using theFFT (Fast Fourier Transform).

Figure 3. Landsat ETM7 image of false color composite(band-1, band-2, band-3) in year of 2001 from MarusuDistrick in South Sulawesi.

3.3. The Checking by Knowledge Representation

Table-1 shows about premis categories for production rulesof network structure of NFES-Model.

Table-1. Premis category for production rules

With the premis category for production rules, then at-tribute items to become “what the pixel value to be appro-priated with the mean symbol in table-1?”. And because

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network structure is consist of three inputs, then atributitems will become 18 kinds (3 x 6). Inference result by for-ward chaining method will be reduced of rules to be except8 rules. If R is value of pixels in band-1, G is value of pixelsin band-2, and B is value of pixels in band-3, then the eightrules each is1. IF R least valueAND G least valueAND B least valueOR R small value

THEN classified is Hutan (forest)2. IF R least value

AND G least valueOR G small valueOR G medium small

AND B small valueOR B medium smallOR B medium value

THEN classified is Air (water)3. IF R least value

OR R small valueAND G least value

OR G small valueAND B least value

THEN classified is Tegalan/kebun (garden)4. IF R small value

OR R medium smallOR R medium value

AND G least valueOR G small valueOR G medium small

AND B small valueOR B medium smallOR B medium value

THEN classified is Tambak (embankment)5. IF R small value

OR R medium smallOR R medium value

AND G small valueOR G medium smallOR G medium value

AND B least valueOR B small valueOR B medium small

THEN classified is Sawah (paddy field)6. IF R medium small

OR R medium valueAND G small value

OR G medium small

AND B medium smallOR B medium value

THEN classified is pemukiman (urban)7. IF R medium value

AND G medium valueOR G large value

AND B large valueTHEN classified is lahan gundul (bare land)

8. IF R large valueAND G medium value

OR G large valueAND B medium value

OR B large valueTHEN classified is awan (cloud).

The visualization of all rules in the computer screen can beseen as in figure-4.

Figure-4. The network structure of NFES-Model createdby rule production

3.4. Fast Fourier Transform Algorithm

If a function )(tf is periodic with period T, then it can beexpressed as an infinite sum of complex exponentials inthe manner

TeFtf o

n

tjnn

oπωω 2,)( == ∑

−∞=

(3)

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in which n is an integer, oω is an angular frequence andthe complex expansion coefficients Fn (Fourier series) aregiven by

dtetfT

FT

T

tjnn

o∫−

−=2/

2/

)(1 ω

(4)

The transform itself, which is equivalent to the Fourierseries coefficients of (4), is defined by

dtetfF tj∫∞

∞−

−= ωω )()(

(5)

Let the sequence )(kφ , k = 0,…,K-1 be the set of Ksamples taken of f(t) over the sampling period 0 to To. The

samples correspond to times . The continuousfunction f(t) is replaced by the samples and is replaced by, with r = 0,1,…,K-1. Thus . The time variable t is replacedby , k = o,…,K-1. With these changes (5) can be written insampled form as

, r = 0,…,K-1 (6)with

.(7)

It is convenient to consider the reduced form of (6):

, r = 0,…,K-1 (8)

Assume K is even; in fact the algorithm to follow will re-quire K to be expressible as K = 2m where m is an integer.From form two sequences Y(k) and Z(k) each of K/2samples. The first contains the even numbered samples ofand second the odd numbered samples,

Y(k): f(0), f(2), …, f(K-2)Z(k): f(1), f(3), …, f(K-1)

So thatY(k) = f(2k)Z(k) = f(2k + 1) , k = 0,…, (K/2)-1.

Equation (8) can then be written=

=

=(9)

Where B(r) and C(r) will be recognized as the discrete Fou-rier transform of the sequences Y(k) and Z(k).

4. Discussion and Result

A supervised learning algorithm of NFES to adapted thefuzzy sets is continuously a cyclic via learning sets untilhas obtain the final criterion is appropriate, for example, ifnumber of misclassification to indicate the value is accep-tance well be reached, or error value can’t decrease again. In table-2, presented the land cover classification resultin Marusu District, South Sulawesi, Indonesia, and landcover area with the assumption that each pixels look afterthe interest of 30 m2 area of land. Classification using NFESin a form of knowledge based expert system. Developmentof the rule base referred by two circumstances, namely, themap information (earth form) and geographic knowledge.

As the implemented of the NFES-Model, we are demon-strated of the image classified result is showed in figure-4,where the image classification are consist of water area(Air), forest area (Hutan), paddy field area (Sawah), em-bankment area (Tambak), garden area (Tegalan/Kebun),urban area (Urban), bare land area (Lgdl), and cloud area(Awan). From the calculation result, we are obtained thetabulation data of classified result as showed in table-2.The training error by back-propagation method to get valueis 6.6365 for 100 iteration, and error 0.68957 for 1000 itera-tion. Then with used the method is propose, we are obtainerror at same 0.00013376.

Table-2. Calculation ResultObject Number of pixels Areas (ha)Water (Air)Forest (Hutan)Paddy field (Sawah)Embankment(Tambak)Garden (Kebun)Urban (Pemukiman)Bare land(Lahan gundul/Lgdl)The covered of Cloud(Awan)Classified areaSurvey area (size: 483 x 381)Unclas-sified areaPercent classifiedPercent unclassified525635994018720147893363280144293871182553184023147099.20%0.80 %1576917982561644371009840412882615476655207441

Figure-5. The land covers classification of Marusu, SouthSulawesi , Indonesia

5. Conclusion

Verification result using by NFES-Model to land cover clas-

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ploma, Braunschweig, 1999.[10] Simpson, J.J. and Keller, R.H., An Improved Fuzzy Logic

Segmentation of Sea Ice, Clouds, and Ocean inRemotely Sensed Arctic Imagery, Remote Sens.Environ., 1995, Vol.54, pp. 290 – 312.

Figure-6a. The signal checking of an original image

Figure-6b. The signal checking of the image dataclassification

Figure-6c. The signal checking of the image dataclassification with the stegano

Figure-7a. The image data classification without thestegano

Figure-7b. The image data classification with thestegano

sified has been showed decreases of misclassification. Withusing artificial neural network approximation, or back-propagation neural network (BPNN), misclassification upto 20% (investigation result to obtain 12.29%), then ifusing the NFES-Model, with the test-case using LandSat-ETM7 data of Marusu District, South Sulawesi,misclassification is 0.8% only.The signal checking of the original image, the image dataclassification, and the image data classification with thestegano/secret information are showed in Figure-6 andFigure-7.

REFERENCES

[1] Funabashi, M. et al., Fuzzy and neural hybrid expertsystem: Synergetic AI, IEEE Expert, 1995, pp. 32-40.

[2] Skidmore et al, An operational GIS expert system formapping forest soil, Photogrammetric Engineering& Remote Sensing, 1996, Vol.62, No.5, pp. 501-511.

[3] Maeda, A. et al., A fuzzy-based expert system buildingtool with self-tuning capability for membershipfunction, Proc. World Congress on Expert Systems,Pergamon Press, New York, 1991, pp. 639-647.

[4] Murai, H., Omatu, S., Remote sensing image analysisusing a neural network and knowledge-based pro-cessing”, Int. J. Remote Sensing, 1997, Vol.18, No.4,pp. 811-828.

[5] Jang, J. S. R., ANFIS: Adaptive-Network-based FuzzyInference Systems, IEEE Transactions on Systems,Man, and Cybernetics, 1993, Vol. 23, No. 3, pp. 665-685.

[6] Prasad, S.A., Sadly, M., Sardy, S., Landsat TM Imagedata Classification of Land Cover by Fuzzy C-Mean, Proc of the Int. Conf. on Opto-electronicsand Laser Applications ICOLA’02, pp. D36-D39,October 2-3, 2002, Jakarta, Indonesia. (ISSN : 979-8575-03-2)

[7] Sangthongpraow, U., Thitimajshima, P., andRangsangseri, Y., Modified Fuzzy C-Means for Sat-ellite Image Segmentation, GISdevelopment.net,1999.

[8] Enbutu, I. Et al., Integration of multi-AI paradigms forintelligent operation support systems: Fuzzy ruleextraction from a neural network, Water Scienceand Technology, 1994, Vol. 28, no. 11-12, pp. 333-340.

[9] Nauck, U., Kruse, R., Design and implementation of aneuro-fuzzy data analysis tool in java, Thesis Di-

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Jurusan Teknik Informatika Universitas Katolik Widya Mandira KupangEmail: [email protected]

AbstractAs a combinatorial problem, university examination timetabling problem is known to be NPcomplete, and is defined asthe assignment of a set of exams to resources (timeslots and rooms) subject to a set of constraints. The set of constraintscan be categorized into two types; hard and soft. Hard constraints are those constraints that must by compulsoryfulfilled. Soft constraints are non-compulsory requirements: even though they can be violated, the objective is tominimize the number of such violations. The focus of this paper is on the optimization problem, where the objective is tofind feasible solution i.e. solution without hard constraint violation, with minimum soft constraint violations. Someheuristics based on the simulated annealing (SA) are developed using three neighborhood structures to tackle theproblem. All heuristics contain three phases, first a feasible solution is sought using a constructive heuristic, followedby the implementation of SA heuristics using single neighborhood. In Phase 2, a hybrid SA is used to further minimizethe soft constraint violations. In Phase 3 the hybrid SA is run again several times using different random seeds toproceed on the solution provided by Phase 1. The heuristics are tested in the instances found in the literature and theresults are compared with several other authors. In most cases the performance of the heuristics are comparable to thecurrent best results and they even can improve the state-of-the art results in many instances.

Keyword: Computational Intelligence, Simulated Annealing, Heuristic/ Metaheuristic, Algorithm, UniversityExamination Timetabling, Timetabling/ scheduling, Combinatorial Optimization

1. INTRODUCTION

Saturday, August 8, 200916:00 - 16:20 Room L-212

A THREE PHASE SA–BASED HEURISTIC FOR SOLVINGA UNIVERSITY EXAM TIMETABLING PROBLEM

The goal of an Examination Timetabling Problem (ExTP) isthe assignment of exams to timeslots and rooms, and themain requirement is that no students nor invigilatorsshould be assigned to more than one room at the sametime. Among the most representative variants of ExTPs, itmust be cited the uncapacitated and capacitated ones. Inthe uncapacitated version, the number of students andexams in any time slot is unlimited. Meanwhile, thecapacitated version imposes limitations on the number ofstudents assigned to every timeslot. Also, another pointworthy of mention is that the uncapacitated version ofexamination timetabling is divided into two problems, i.euncapacitated with and without cost. The uncapacitatedwithout cost problem can be transformed into a GraphColoring Problem and has been addressed by the authorsin [16]. In this paper the uncapacitated with cost problemwill be addressed. A more complete list of examinationtimetabling variants can be found in[12].

2. Problem Formulation and Solution RepresentationAny instance of this problem will contain a set of eventsor exams, a set of resources and a set of constraints. Forinstance I, let V = {v1, v2,…, vn} be the set of events(exams) that have to be scheduled. Let G=(V,E) be a graphwhose vertices are the set of V , and {vi,vj} ??E(G) if andonly if events vi and vj are in conflict. The graph G iscalled the conflict graph of I.The set of rooms is denoted by R, the set of timeslots isdenoted by W and let B = R x W be a Cartesian productover the set of rooms and the set of time slots. The set Bis the resource set of the instance. In our implementation,each member of B will be represented by a unique integer,in such a way that it is easy to recover which room andtime slot a resource belongs to.For each event vi, there is a specific set Di?B, whichcontains all candidate resources that could be used by

Mauritsius Tuga

179200

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event vi and in general, Di _ Dj, “i _ j. Let the domainmatrix D is an adaptive matrix contains |V| rows and eachrow is made of the set Di. This matrix will be used tocontrol the hard constraints. The soft constraints will berepresented as an objective function as described inSection 5.The problem is to assign each event to its resource fromits domain while minimizing the objective function.A solution or a timetable is represented by a onedimensional array L where L(i)=b means event or exam Iis assigned to resource b.

3. Techniques applied to Examination Timetabling ProblemsThere are a significant number of researchers focusing onthis problem. In fact, this problem have been attractingthe highest number of researchers in the timetablingresearch area [12]. Some approaches that produced thebest solution(s) for at least one of the instances we areusing, according to the survey in [12] will be discussednext.

Merlot et al. [11] used three-stage approach; constraintprogramming to generate initial solutions, followed bySimulated Annealing and Hill Climbing.Yang et al. [17] applied a Case Base Reasoning (CBR)strategy to solve the problem. To minimize the softconstraint violations, they used the so called “GreatDeluge” algorithm which is another metaheuristic verysimilar to SA. Abdullah et al. [2] implemented a local searchbased heuristic using a large neighborhood structurecalled Ahuja-Orlin’s neighborhood structure. The“Exponential Monte Carlo” acceptance criterion was usedto deal with non-improving solutions. An application ofheuristics based on multi-neighborhood structures wasused by Burke et al. [4]. In this strategy, they applied localsearches using several neighborhood structures.

4. SA-based Heuristics

In this work the timetabling process is carried out in threephases.

4.1 Phase 1. Constructive Heuristic (CH) + Single SA.A Constructive Heuristic (CH) that is similar to the one in[15,16] is used to find initial feasible solutions.The feasible solutions found by this heuristic will befurther processed by a SA-based method to minimize thenumber of soft constraint violations. Many kinds of SAusing the following three neighborhood structures aretested.

1. Simple neighborhood: This neighborhood containssolutions that can be obtained by simply changing theresource of one event.2. Swap neighborhood: Under the simple neighborhood,an exam is randomly chosen and a new resource is allocatedto it. However, this may involve some bias as there mightbe events which do not have any valid resources left atone stage of the search. This might create a disconnectedsearch space. In the swap neighborhood, the resourcesof two events are exchanged, overcoming thedisconnection of the search space that might occur in thesimple neighborhood.

3. Kempe chain neighborhood operates over two selectedtimeslots and was used in [4,11,14] to tackle examinationtimetabling problems. It swaps the timeslot of a subset ofevents in such a way that the feasibility is maintained.Figure 1. A bipartite graph induced by the events assignedto Timeslot T1 and T2 before moving the Event 3 toTimeslot T2 To illustrate the idea, assume that a solutionof an examination timetabling problem assigns Event1,3,5,7,9 to Timeslot T1 and Event 2,4,6,8,10 to TimeslotT2 (Figure 1). The lines connecting two events indicatethe corresponding events are in conflict. By consideringevents as vertices and the lines as edges, this assignmentinduces a bipartite graph. A Kempe chain is actually aconnected subgraph of the graph. If we choose forexample Event 3 in T1 to move to T2, then to keep thefeasibility, all events in the chain containing Event 3 haveto be reassigned. In this case, Event 2 and 8 have to bemoved to Timeslot T1 and Event 7 has to be moved to T2.After the move is made, another feasible assignment isobtained (Figure 2).

Figure 2. A bipartite graph induced by the events assignedin Timeslot T1 and T2 after moving Event 3 in Figure 1 toTimeslot T2 This example shows how a Kempe chain movecan be made triggered by the Event 3 in Timeslot T1.Different new feasible solution may be obtained if wechoose another event as trigger. However, if the chosentrigger in the last example is Event 7 instead of Event 3, wewill obtain the same chain, and end up with the same newfeasible solution.Based on these three neighborhood structures some SAheuristics are developed; called simple SA, Swap SA13572468

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109T1 T212589346710T1 T2and Kempe SA each of which uses simple, swap andKempe chain neighborhood respectively. This group ofSAs is referred to as single SA as they use only oneneighborhood structure. After conducting some tests itbecomes clear that the SA using the Kempe chainneighborhood structure is the most suitable SA to be usedin this Phase. The use of the CH followed by Kempe SAwill be referred to as Phase 1 heuristic.

4.2 Phase 2. Hybrid Simulated AnnealingThe use of Single SAs and a Hybrid SA called HybridSAare tested in this Phase. HybridSA is a SA using twoneighborhood structures, embedded by the Kempe ChainHill Climbing Heuristics ( KCHeuristics).The neighborhood used in the SA part are simpleneighborhood and swap neighborhood. The pseudocodefor Hybrid SA is presented in Figure 3.Figure 3. The pseudocode for HybridSA Based on sometests, the SA process using a Kempe chain neighborhood- in the first stage, followed by the HybridSA in the secondstage seems to be the best combination for the problem.The implementation of Phase 1 heuristic followed by theHybridSA will be called Phase 2 heuristic.

4.3 Phase 3. Extended HybridSAThe Extended HybridSA is essentially an extension ofPhase 2. In this phase the Hybrid SA in the second phaseis rerun several times fed by the same good solutionprovided by the Phase 1 heuristic. That is, after runningPhase 1 and Phase 2 several times, the solution found bythe Phase 1 heuristic which gives the best solution inPhase 2 will be recorded. Subsequently, the HybridSA isrerun 10 times using the recorded solution with differentrandom seeds. This will be referred to as

Phase 3 heuristic.The reason behind this experiment is that the solutionsproduced by the Kempe SA in the first phase must begood solutions representing a promising area.

This area should thus be exploited more effectively tofind other good solutions. We assume that this can bedone by the HybridSA procedure effectively.

4.4 Cooling scheduleThe cooling schedule used in all scenarios of the SAheuristics are very sensitive. Despite many authors haveinvestigated this aspect it turned out that none of theirrecommendations was suitable for the problem at hand.Some preliminary tests then had to be carried out to tunein the cooling schedule.

1. Initial temperature:is the average increase in cost and is the acceptance ratio,defined as the number of accepted nonimproving solutionsdivided by the number of attempted non-improvingsolutions. The acceptance ratio is chosen at random withinthe interval [0.4, 0.8] . A random walk is used to obtainthe .

2. Cooling equation: Many cooling equations are testedand found out that the best one is the following.The value for _ is chosen within the interval [0.001,0,005].

3. Number of trials: The number of trials in each temperaturelevel is set to a|V| where a is linearly increased. Initially, ais initially set to 10.As in [16] there is a problem of determining the temperaturewhen a solution is passed to a Simulated Annealingheuristic. The problem is to set a suitable temperature sothe annealing process can continue to produce other goodsolutions. We have developed an alternative temperatureestimator based on the data obtained from running thesingle SA heuristic using simple neighborhood structuresfor each instance. We found that using a quadraticfunction that relates temperature and cost seems to bethe most suitable one.This is empirically justified based on observationsgathered from the tests.

5. Computational Experiments5.1 The InstancesThe instances used for the examination timetablingproblem derive from real problems taken from institutionsworldwide and can be downloaded from http://www.cs.nott.ac.uk/~rxq/data.htm. These instances wereintroduced by Carter et al. [8] in 1996 and in turn weretaken from eight Canadian academic institutions; from theKing Fahd University of Petroleum and Minerals inDhahran; and from Purdue University, Indiana, USA. Thenumber of exams are ranging from 81 to 2419 and thenumber of students that are to be scheduled ranging from

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611 to 30.032 students. The number of timeslots variesfrom instance to instance and is part of the originalrequirements posed by the university.In this paper we address the uncapacitated with costproblem, where the objective is to find a feasible solutionwith lowest cost. In this problem, the hard constraint isthere is no student conflict. Any number of events can beassigned to a timeslot as long as there is no student clash.The soft constraint for this problem states that thestudents should not sit in exams scheduled too close oneto another, i.e. the conflicting exams have to be spreadout as far as possible. The objective function for thisproblem is recognized as a proximity cost and was posedby Carter et al. [8]. Let S be the set of students and Vi bethe set of exams taken by the student i. Let also vij denotethe j-th exam of student i. Given an exam timetable L, theobjective function is:

5.2 Computational Results and AnalysisAll tests were run on a PC Pentium IV 3.2 GHz runningunder Linux. In this problem the execution time for thetests were relaxed. Here we used the number of iterationswhere the improvement could not be found. However, toavoid an extremely long execution, for eachinstance we set the maximum time length at 10,000 seconds.Experiments conducted by other authors reportedly spentmore time than we used for each instance. Abdullah et al.[2] reported that their approaches were run overnight foreach instance.The CH process is able to generate the feasible initialsolutions for all instances using the given number oftimeslots within just a few seconds. The three heuristicsare effective in tackling the problem.The use of HybridSA in Phase 2 significantly improve thesolutions found in the first phase in all instances. But toreach those costs, the CPU time consumed is almost twotimes larger than that of Phase 1. The CPU time for thesetwo phases vary considerably depending on theinstances, being as fast as 152 seconds and up to 10,000seconds..Instance name BurkeEt al. [18]MerlotEt al. [11]BurkeEt al.[7]BurkeEt al.[6]AsmuniEt al.[3]KendallEt al.[10]Car91 4.65 5.1 5.0 4.8 5.29 5.37

Car92 4.1 4.3 4.3 4.2 4.56 4.67Ear83 37.05 35.1 36.2 35.4 37.02 40.18Hec92 11.54 10.6 11.6 10.8 11.78 11.86Kfu93 13.9 13.5 15.0 13.7 15.81 15.84Lse91 10.82 10.5 11.0 10.4 12.09 -Pur93 - - - - - -Rye92 - - - 8.9 10.35 -Sta83 168.93 157.3 161.9 159.1 160.42 157.38Tre92 8.35 8.4 8.4 8.3 8.67 8.39Uta92 3.2 3.5 3.4 3.4 3.57 -Ute92 25.83 25.1 27.4 25.7 27.78 27.6Yor83 37.28 37.4 40.8 36.7 40.66 -Instance name YangEt al.[17]AbdullahEt al.[2]BurkeEt al.[5]BurkeEt al.[4]Our ResultsHybridSA Ext.HybridSACar91 4.5 5.2 5.36 4.6 4.49 4.42Car92 3.93 4.4 4.53 4.0 3.92 3.88Ear83 33.7 34.9 37.92 32.8 32.78 32.77Hec92 10.83 10.3 12.25 10.0 10.47 10.09Kfu93 13.82 13.5 15.2 13.0 13.01 13.01Lse91 10.35 10.2 11.33 10.0 10.15 10.00Pur93 - - - - 4.71 4.64Rye92 8.53 8.7 - - 8.16 8.15Sta83 158.35 159.2 158.19 159.9 157.03 157.03Tre92 7.92 8.4 8.92 7.9 7.75 7.74Uta92 3.14 3.6 3.88 3.2 3.17 3.17Ute92 25.39 26.0 28.01 24.8 24.81 24.78Yor83 36.35 36.2 41.37 37.28 34.86 34.85

Tabel 1. Comparison between the normalized cost obtainedby the SA heuristics and some current results onExamination Uncapacitated with Cost Problem. The valuesmarked in bold italic indicate the best cost for thecorresponding instance.Also, it is to be noted that the Extended HybridSA heuristicin Phase 3 gives another contribution to tackle the problem,as it can still improve the solution found in Phase 2.Unfortunately due to the limitation on the report lengthno table can be presented to compare the results gainedfrom each phase. However, in Table 1 we present our bestresults alongside results found in the literature. The datafor the following table was taken from the surveyconducted by Qu et al. [12]. Note that there are actuallymore reports in this same problem presented in [12].However, as pointed out by Qu et al. [12], some of thereports might have used different versions of the data

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set, or even addressed a different problem. We thereforedo not include those reports in the comparison shown inTable 1.Table 1 shows that compared to the other methods, thethree-phase SA is very robust in handling this problem. Itis able to produce the best solution found so far andsometimes improves the quality of the solutions alreadyfound in many instances.It only fails to improve the result for the instance Uta92by a very small difference to the current best cost Notethat due to the different application of the rounding system,we do not know the exact relative position on theachievement in instances Hec92, Kfu93 and Lse91compared to those found by Burke et al. [4]. It is alsoworth mentioning that without the Extended HybridSA,the HybridSA heuristic itself can improve the current stateof the art results in many instances (Table1).

6. CONCLUSION

With this data set the HybridSA in collaboration with thePhase 1 heuristic performed well and the results werecomparable to other methods. However, this approach wasnot robust enough to handle the problem.The extended HybridSA seemed to be a better choice forthis kind of problem. It could match the best cost foundso far in the literature, and even improve the quality of thesolutions for many instances.

7. REFERENCES

[1] S. Abdullah, E.K. Burke, B. McCollum, 2005, AnInvestigating of Variable NeighborhoodSearch forUniversity Course Timetabling, in Proceeedingsof Mista 2005: The 2nd MultidisciplinaryConference on Scheduling: Theory andApplications, New York, pp.413-427.

[2] S. Abdullah, S. Ahmadi, E.K. Burke, M. Dror,(2007),Investigating Ahuja-Orlin’s Large NeighborhoodSearch Approach for Examination Timetabling,Operation Research Spectrum 29) pp.351-372.

[3] H. Asmuni, E.K. Burke, J. Garibaldi and B. McCollum,in: E. Burke and M. Trick (Eds.): PATAT2004,Lecture Notes in Computer Science, 3616,Springer-Verlag Berlin Heidelberg 2005, (2005) pp.334-353.

[4] E.K. Burke, A.J. Eckersley, B. McCollum, S. Petrovic, R.Qu, ,(2006).Hybrid Variable NeighborhoodApproaches to University Exam Timetabling,Technical Report NOTTCS-TR-2006-2, School ofCSiT University of Nottingham.

[5] E.K. Burke, B. McCollum, A. Meisels, S. Petovic,R. Qu,A Graph-Based Hyperheuristic for EducationalTimetabling Problems, European Journal ofOperational

Research 176(2007) pp.177-192.

[6] E.K. Burke, Y. Bykov, J. Newall, S. Petrovic, A Time-Predefined Local Search Approach to ExamTimetabling Problems. IIE Transactions 36 6(2004)pp. 509-528.

[7] E.K. Burke, J.P. Newall, (2004) Solving ExaminationTimetabling Problems through Adaption ofHeuristic Orderings, Annals of OperationalResearch 129 pp. 107-134.

[8] M.W. Carter, G. Laporte, S.Y. Lee, (1996) ExaminationTimetabling: Algorithmic Strategies andApplications,Journal of Operational ResearchSociety ,47 pp.373-383.

[9] S. Even, A. Itai, A. Shamir, (1976) On the Complexity ofTimetabling and Multicommodity Flow Problems,Siam Journal on Computing 5(4) pp. 691-703.

[10] G. Kendall , N.M. Hussin, An Investigation of a TabuSearch based Hyperheuristic for ExaminationTimetabling, In: Kendall G., Burke E., Petrovic S.(eds.), Selected papers from MultidisciplinaryScheduling; Theory and Applications, (2005) pp.309- 328.

[11] L.T.G. Merlot, N. Boland, B.D. Hughes, P.J. Stuckey,(2001) A Hybrid Algorithm for the ExaminationTimetabling Problems, in: E. Burke and W. Erben(Eds.): PATAT 2000,Lecture Notes in ComputerScience, 2079, Springer-Verlag Berlin Heidelberg2001, pp 322-341.

[12] R. Qu, E.K. Burke, B. McCollum, (2006).A Survey ofSearch Methodologies and AutomatedApproaches for Examination Timetabling,Computer Science Technical Report No. NOTTCS-TR-2006-4

[13] J. Thompson and K. Dowsland. General collingschedules for a simulated annealing basedtimetabling system. In E. Burke and P. Ross,editors, Proceedings of PATAT’95, volume 1153of Lecture Notes in Computer Science, pages 345–363. Springer-Verlag, Berlin, 1995

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[14] J.M. Thompson, K.A. Dowsland, (1998) A RobustSimulated Annealing Based ExaminationTimetabling System, Computers Ops.Res. Vol.25No.7/8 pp.637-648.

[15] M. Tuga, R. Berretta, A. Mendes, (2007), A HybridSimulated Annealing with Kempe ChainNeighborhood for the University TimetablingProblem, Proc. 6th IEEE/ACIS InternationalConference on Computer and Information Science,11-13 July 2007, Melbourne-Australia.pp.400-405.

[16] M. Tuga, Iterative Simulated Annealing Heuristicsfor Minimizing the Timeslots Used in a UniversityExamination Problem, submitted to IIS09Yogyakarta 2009.

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[17] Y. Yang, S. Petrovic, (2004) A Novel Similarity Measurefor Heuistic Selection in Examination Timetabling,in: E.K. Burke, M. Trick (Eds), Selected Papersfrom the 5th International Conference on thePractice and Theory of Automated TimetablingIII, Lecture Notes in Computer Science, 3616,Springer-Verlag, pp.377-396.

[18]E.K. Burke, J.P. Newall, Enhancing Timetable Solutionswith Local Search Methods, in: E. Burke and P. DeCausmaecker (Eds.), PATAT 2002,Lecture Notesin Computer Science, 2740, Springer-Verlag, (2003)pp. 195-206.

.

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Paper

AbstractA convergence of IP and Television networks, known as IPTV, gains popularity. Unfortunately, today’s IPTV has limita-tion, such as using dedicated private IPv4 network, and mostly not considering “quality of service”. With the availabilityof higher speed Internet and the implementation of IPv6 protocol with advanced features, IPTV will become broadlyaccessible with better quality. IPv6 has a feature of Quality of Service through the use of its attributes of traffic class orflowlable. Existing implementation of current IPv6 attributes is only to differentiate multicast multimedia stream and nonmulticast one, or providing the same Quality of Service on a single multicast stream along its deliveries regardlessnumber of subscribers. Problem arose when sending multiple multicast streams on allocated bandwidth capacity anddifferent number of subscribers behind routers. Thus, it needs a quality of service which operates on priority based formultiple multicast streams. This paper proposes a QoS mechanism to overcome the problem. The proposed QoS mecha-nism consists of QoS structure using IPv6 QoS extension header (generated by IPTV provider) and QoS algorithm inexecuted in routers. By using 70% configuration criteria level and five mathematical function models for number ofsubscribers, our experiment showed that the proposed mechanism works well with acceptable throughput.

Index Terms— IPTV, IPv6, Multiple Multicast Streams, QoS Mechanism

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QoS Mechanism With Probability for IPv6-Based IPTV Network

I. INTRODUCTIONA convergence of two prominent network technologies,which are Internet and television, as known as InternetProtocol Television (IPTV), gains popularity in recent years,as in July 2008 Reuters’ television survey reported thatone out of five American people watched online television[1]. With the availability of higher speed Internet connec-tion, the IPTV becomes greatly supported for better qual-ity.IPTV provides digital television programs which are dis-tributed via Internet to subscribers. It is different from con-ventional television network, the advantage of operatingan IPTV is that subscribers can interactively select televi-sion programs offered by an IPTV provider as they wish[2]. Subscribers can view the programs either using a com-puter or a normal television with a set top box (STB) con-nected to the Internet.

Ramirez in [3], stated that there are two types of servicesoffered to IPTV subscribers. First, an IPTV provider offersits contents like what conventional television does. TheIPTV broadcaster streams contents continuously on pro-vided network, and subscribers may select a channel in-teractively. The data streams are sent in a multicast way.The other one is that an IPTV provider with Video on De-mands (VOD) offers its content to be downloaded partlyor entirely until the data videos are ready for subscribersto view. The data are sent in a unicast way.Currently IPTV is mostly operated in IPv4 network and it isprivately managed. Therefore, IPTV does not provide“quality of service” (QoS) for its network performance andIPTV simply uses “best effort” [2]. Since the privately man-aged network offers a huge and very reliable bandwidth

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Primantara H.S, Armanda C.C, Rahmat BudiartoSchool of Computer Sciences, Univeristi Sains Malaysia, Penang, [email protected], [email protected], [email protected],

Tri Kuntoro P.School of Computer Science, Gajah Mada University, Yogyakarta, Indonesia

[email protected]

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for delivering IPTV provider’s multicast streams (channels)[4] and IPTV subscribers are located relatively closer toIPTV providers [2], the IPTV network performance is excel-lent.In near future, the IPv4 address spaces will be no longeravailable. Moreover, there is a need to implement “qualityof service” as IPTV protocol is possibly implemented inopen public network (Internet) rather than in its privatenetwork to obtain more ubiquitous subscribers.The solution to this problem is the use of IPv6 protocol.IPv6 not only providing a lot of address spaces, but it alsohas more features, such as security, simple IP header forfaster routing, extension header, mobility and quality ofservices (QoS) [5,6]. The use of QoS in IPv6 needs to uti-lize attributes of flowlable or traffic class of IPv6 header[5]. In addition, since an IPTV is operated as a standardtelevision on which multiple viewers possibly watch thesame channel (multicast stream) from the same IPTV ser-vice provider, the IPTV stream has to be multicast in orderto save bandwidth and to simplify stream sending pro-cess. IPv6 is capable of providing these multicast streamdeliveries.In addition to IPTV’s unicast VOD deliveries, an IPTV pro-vider serves multiple channels. Each channel sends amulticast stream. On the other hand, IPTV’s subscribersmay view more than one different channel which can befrom the same or different IPTV providers. Therefore, eachmulticast stream (channel) may have different number ofsubscribers. Further more, even in a multicast stream, thenumber of its subscribers under a router and another routercan be different.The use of current IPv6’s QoS which employs flowlable ortraffic class attributes of IPv6 header is suitable for singlemulticast stream delivery. Meanwhile, IPTV provider needsto broadcast multiple multicast streams (channels). Withregards to the various numbers of subscribers joiningmultiple multicast streams, then the QoS for each multicaststream would be differentiated appropriately. Thus, thecurrent IPv6’s QoS could not be implemented on multiplemulticast streams, even though it uses Per Hop Behavior(PHB) on each router [7,8,9]. This is because the multicaststream will be treated a same “quality of service” on eachrouter, regardless the number of subscribers of the multicaststream exist behind routers.The solution to this problem is to use another mechanismto enable operation of QoS mechanisms for multiplemulticast streams with also regards to the number of join-ing subscribers on the streams. The proposed mechanismutilizes QoS mechanism which implements a new IPv6 QoSextension header (IPv6 QoS header, for short) as QoS struc-ture, and QoS mechanism to employ such algorithm. IPv6QoS header will be constructed on IPTV provider and at-tached to every multicast stream packet of data, and QoSmechanism is operated on each router to deal with thepacket of data which carry IPv6 QoS header.

The main focus of this research will be on designing QoSmechanism, and evaluating its performance by using NS-3network simulator with regard to QoS measurement, whichincludes throughput, delay and jitter [10,11]. To simulatethe role of the number of subscribers, five mathematicalfunction models are used.

II. RELATED WORK

IPTV high level architecture consists of four main parts,which are content provider, IPTV service provider, net-work provider and subscribers [12]. Firstly, a content pro-vider supplies a range of content packets, such as videoand “traditional” television live streaming. Secondly, anIPTV service provider (IPTV provider in short) sends itscontents to its ubiquitous subscribers. Thirdly, it is a net-work provider which offers network infrastructure to reli-ably deliver packets from an IPTV provider to its subscrib-ers. Finally, subscribers are users or clients who accessthe IPTV contents from an IPTV provider. A typical IPTVinfrastructure, which consists of these four main parts, isshown in Figure 1.

Figure 1. A Typical IPTV Infrastructure [13]

Some researches on IPTV QoS performance and multicaststructure have been conducted. An Italian IPTV providersends about 83 multicast streams [4] on a very reliablenetwork. Each multicast stream with standard video for-mat requires about 3 Mbps of bandwidth capacity [2,4].Meanwhile, two types of QoS are Integrated Services(IntServ) which is end-to-end base, and Differentiated Ser-vices (DiffServ) which is per-hop base [14,15]. A surpris-ing research work on multicast tree’s size and structure onthe Internet has been conducted by Dolev, et. al. [16]. Theobserved multicast tree was committed as a form SingleSource Multicast with Shortest Path Tree (SPT). The au-thors significantly found that by observing about 1000receivers in a multicast tree, the distance between root andreceivers was 6 hops taken by most number of clients. The

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highest distance taken from the observation was about 10hops.

III. QoS MECHANISM

The proposed QoS mechanism consists of two parts,which are QoS structure as IPv6 QoS extension headerand QoS mechanism executed in each router.

III.1 IPv6 QoS Extension Header on Multicast Stream PacketEach multicast stream’s packet of data needs to carry theIPv6 QoS header with the purpose of enabling intermedi-ate routers all the way to reach IPTV’s subscribers. Thestructure of IPv6 QoS header is shown in Figure 2, thatalso shows the location of the IPv6 QoS header in IPv6datagram.

Figure 2. IPv6 QoS extension header

Each IPv6 QoS header, which is derived from standardIPv6 extension header format, maintains a number of QoSvalue structures. QoS value defines attributes of networkaddress (64 bits), netmask (4 bits) and QoS (4 bit). Networkaddress is an address of the “next” link connected to therouter. Netmask is related to network address’s netmask.QoS is the value of priority level. This QoS is calculatedwith formulae in equation 1.

⎥⎦

⎥⎢⎣

⎢= 16x

NN

QoStot

dwvalue (1)

where :Ndw : Number of all subscribers only “under” the router

Ntot : Number of total subscribers request-

ing the streams.Based on these values, an intermediate router knows howto prioritize forwarding an incoming multicast stream withthe QoS value.

III.2 QoS Mechanism on Intermediate RouterQoS mechanism works as Queuing and Scheduling algo-rithm to run a forwarding policy to perform DiffServ. Everyconnected link to a router has different independent queu-ing and scheduling. Thus, any incoming multicast streamcan be copied into several queuing and scheduling pro-cess.The algorithm for queuing and scheduling is composed ofthree parts as follows.a. Switching and queuing any incoming streamThis part aims to place the stream into appropriate queueby reading the QoS value in IPv6 QoS extension header.The algorithm for switching and queuing is shown in Fig-ure 3.

Figure 3. Switching and Queuing Algorithm

b. QueueQueue consists of N number of queue priority levels. Ev-ery level is a queue which can hold incoming multicaststream to be forwarded. The priority queue levels are basedon QoS value. These levels are shown in Figure 4.

Figure 4. Queue Priority Levels

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c. SchedulingScheduling for datagram forwarding is to select a queuefrom which a dequeuing process to forward a queuedmulticast datagram to corresponding link occurs. The al-gorithm is shown in Figure 5.

Figure 5. Scheduling Algorithm

IV. ExperimentS

The experiments are conducted by using NS-3 networksimulator to measure IPTV performance with regard to QoSmeasurements (delay, jitter and throughput). However,before doing experiments, some steps are carried out whichinclude configuring network topology, setting up QoSmechanism for each router, and configuring five mathemati-cal function models to represent the models of the num-bers of subscribers joining multicast streams.

IV.1 Network TopologyThe network topology for our simulation is configured asin Figure 6.

Figure 6. Network Topology

Each link shown in Figure 6 is configured with 150 Mbps,except those which are for local are network (LAN1, LAN2and LAN3) and L01. Some links are not necessary, as themulticast tree does not create any “loop”.In this simulation, an IPTV Multicast Stream Server gener-ates about 50 multicast streams and 8 unicast traffics torepresent IPTV channels and VoDs, respectively. Eachmulticast stream is generated as constant bit rate (CBR) in3 Mbps, and also for each unicast traffic as well. There-fore, the total of bandwidth required to send all traffic isgreater than the available bandwidth capacities of links.Consequently, some traffic will not be forwarded by a router.

IV.2 Setting Up QoS Mechanism on RoutersEach router in this simulation is equipped with the QoSmechanism configuration. QoS mechanism is composedof 16 QoS priority level queues, or 17 QoS priority levelqueues if there is unicast traffic which is placed in thelowest level. In addition, Criteria level is set to 70%. Itmeans that if the 70% of total of all queue size is occupied,then the next incoming packet will be placed into appropri-ate queue priority level based on probability of its QoSvalue. Ciriteria level 70% is a mix between using priorityand probability with a tendency to employ priority mecha-nism. The 70% criteria level is to show that priority is moreimportant than the probability.

IV.3 Models of the Numbers of SubscribersThe numbers of subscribers for multicast streams to easethe evaluation of QoS measurement are modeled into fivemathematical function models. Each model defines howthe numbers of subscribers which are represented by QoSpriority levels are related to the number of multicast streams.For example, a constant model means each QoS prioritylevel has the same number of multicast streams. For in-stance, three multicast streams per QoS priority level.Therefore, it would be a total of 48 multicast streams for all16 QoS priority levels.

Table 1. Five Mathematical Function Models

V. RESULT and Discussion

The result of the experiments is shown in Figure 7 whichdemonstrates the tracing file. On highlighted part, it showsabout a content of simulated multicast stream packet ofdata.

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Figure 7. Tracing File of Simulated Network

Other results are based on QoS measurements on a nodein nearest network (receiving multiple multicast streams)and a node (receiving unicast streams) in the same net-work. The results are shown in Figure 8 to Figure 10.

Figure 8. Average Delay of a Node Receiving MulticastStreams and a Node Receiving Unicast Traffic

Figure 9. Average Jitter of a Node Receiving MulticastStreams and a Node Receiving Unicast Traffic

Figure 10. Throughput of a Node Receiving MulticastStreams and a Node Receiving Unicast Traffic

The most important result is throughput, because it is con-sidered the network reliability. Delay and jitter do not con-siderably disrupt the network; it can be overcome by pro-viding more buffers on subscribers’ node.

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Throughputs of multicast streams are above 55% andthroughputs of unicast traffic are about 35 to 60%. Aver-age delays of multicast streams depend on the mathemati-cal function models, whereas average delays of unicasttraffic are almost the same for all unicast traffic. Average ofjitter for both types of traffic is relatively low, and less than50 µs.

VI. CONCLUSION

The proposed QoS mechanism works well as expected.Based on the experiments, with 70% criteria level and fivemathematical function models for subscribers, all type oftraffic can be successfully forwarded with various through-puts which are about 35% to 74%. However, throughputsof unicast traffic are less than multicast streams, becausethe unicast traffic is placed into the lowest queue prioritylevel.

References

[1]Reuters, 2008, “Fifth of TV viewers watching online:survey”, 29 July 2008, [online], www.reuters.com/a r t i c l e / i n t e r n e t N e w s /idUSN2934335520080729?sp=true

[2]Weber, J., and Newberry, T., 2007, “IPTV Crash Course”,New York: McGraw-Hill.

[3]Ramirez, D., 2008, “IPTV security: protecting high-valuedigital contents”, John Wiley.

[4]Imran, K., 2007, “Measurements of Multicast Televisionover IP”, Proceeding 15th IEEE Workshop on Localand Metropolitan Area Networks.

[5]Hagen, S., 2006, “IPv6 Essential”, O’Reilly

[6]Zhang, Y., and Li, Z., 2004, “IPv6 Conformance Testing:Theory And Practice”, IEEE

[7]RFC 2597, 1999, “Assured Forwarding PHB Group”.

[8]RFC 3140, 2001, “Per Hop Behavior IdentificationCodes”.

[9]RFC 3246, 2002, “An Expedited Forwarding PHB (Per-Hop Behavior)”.

[10]Maalaoui, K., Belghith, A., Bonnin, J.M., andTezeghdanti, M., 2005, “Performance evaluation ofQoS routing algorithms”, Proceedings of the ACS/IEEE 2005 international Conference on Computer

Systems and Applications, January 03 - 06, 2005,IEEE Computer Society.

[11]McCabe, J. D., 2007, “Network analysis, architecture,and design”, 3rd ed, Morgan Kaufmann.

[12]ATIS-0800007, 2007, “ATIS IPTV High Level Architec-ture”, ATIS IIF.

[13]Harte, L., 2007, “IPTV Basics : Technology, Operation,and Services”, [online] http://www.althosbooks.com/ipteba1.html

[14]RFC 2210, 1997, “The Use of RSVP with IETF Inte-grated Services”.

[15]RFC 4094, 2005, “Analysis of Existing Quality-of-Ser-vice Signaling Protocols.”.

[16]Dolev, D, Mokryn, O, and Shavitt, Y., 2006, “OnMulticast Trees: Structure and Size Estimation”,IEEE/ACM Transactions On Networking, Vol. 14,No. 3, June 2006

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I. IntroductionSampling frequency is used to set the time interval be-tween two consecutive actions. For 1 kHz, the time inter-val is 1ms. This time interval must be less than the dy-namic behavior of the plant. Good control system mustchoose appropriate sampling frequency in order to getgood performance but this is not enough. The order of theaction on one sampling time must be considered also.This paper evaluates the idea in [1].The goal of the experiment is to implement the idea in [1]and compare it with the ordinary order.

The sampling time might be disturbed by the delay forsome reasons. This makes the system has sampling jitter,control latency jitter, etc. When the delay is equal to thesampling time then we have a problem [2].

If the processes inside one sampling time have periods,then we discuss the multirate sampling control [3]. Here,each process has its own sampling time so we can insertacceptable delay time to compensate the control action.But what if the delay time exceeds the limit?Since sampling time and processes give a lot of influences,we have to choose the sampling time appropriately andconsider the processes and the possibility of delay in the

Paper

Dept. of Electrical Engineering, Petra Christian University, Surabaya - [email protected]

AbstractA control system uses sampling frequency (or time) to set the time interval between two consecutive processes. At leastthere are two alternatives for this sequence. First, the controller reads the present value from sensor and calculates thecontrol action then sends actuation signal. The other alternative is the controller reads the present value, send theactuation signal based on the previous calculation and calculates the present control action. Both techniques have theirown advantages and disadvantages. This paper evaluates which the best alternative for slow response plant, i.e. roomcontrol system. It is controlled by AVR microcontroller connected to PC via RS-232 for data acquisition. The experimentshows that the order of processes has no effect for the slow response plant

Keywords— sampling, temperature, control

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Sampling Technique in Control System for Room Temperature Control

system. Considering this delay and all processes withinone sampling time, [1] proposes another idea to keep theinterval between the processes constant.

A. Alternative IThis alternative is shown in figure 1. The first alternativesends the current control action for the current situation.It makes the control action is relevant to the present value.This is the advantage of this method. Unfortunately, thistechnique cannot guarantee, that the time interval betweentwo control actions is constant. This is due to the fact thatthe controller can receive interrupt signal and it makes thetime consumes for control action calculation is longer thanthe normal way and this is the disadvantage of this alter-native.

B. Alternative IIThis alternative uses little bit different order, i.e. the con-troller reads the present value and sends the previous re-sult of control action calculation [1]. After sending theprevious result, the controller calculates the control ac-tion and keep the result until the next ‘tick’. Figure 2 showsthe timing diagram for this alternative.

Hany Ferdinando, Handy Wicaksono, Darmawan Wangsadiharja

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This alternative guarantees that the time interval as dis-cussed in above section is constant for all processes. Butthe controller uses previous result of control action calcu-lation for current situation. It looks like it does not makesense for the current situation is handled with calculationwhich is made based from previous situation.

II. Design Of The System

The incubator was made of acrylic with dimension 40x30x30cm. It uses LM35 [4 as temperature sensor and three bulbscontrolled via TRIAC [5,6] as heaters. The AVRmicrocontroller [7] is used to read the LM35 voltage withits internal ADC and actuates the heaters. The control al-gorithm used in the AVR is parallel PID controller tunedwith Ziegler-Nichols method [8]. The AVR (programmedwith BASCOM AVR) [9] communicates with PC (pro-grammed with Visual Basic 5) via serial communication fordata acquisition. The PC also initiates the start command.

Figure 1. Alternative 1

Figure 2. Alternative 2

III. EXPERIMENTAL RESULTSAll experiments start and end at the same temperature, i.e.30oC and 32oC and not all results are presented in thispaper

Figure 3a. No interrupt, method 1

Figure 3b. No interrupt, method 2

Figure 3a and 3b showed that both methods have the sameresponse. Method 1 reaches stability after 11 minutes whilemethod 2 in 10 minutes.The next experiments involved disturbance, i.e. by open-ing windows (figure 4a and 4b) and dry ice (figure 5a and5b).

Figure 4a. No interrupt, method 1 with disturbance (win-dow)

Figure 4b. No interrupt, method 2 with disturbance (win-dow)

Experiments in figure 4a and 4b show that the system canhandle disturbance in 4.1 and 4.35 minutes for method 1and 2 respectively.

Figure 5a. No interrupt, method 1 with disturbance (dryice)

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Figure 5b. No interrupt, method 2 with disturbance (dryice)

time to handle this disturbance, i.e. 7 minutes.Table 1. Summary of figure 3 to 5

Next is experiment with high priority Interrupt Service Rou-tine (ISR). It is simulated with random delay and its valuewill not exceed certain limit such that all processes is stillwithin one sampling time and it will be called interrupt type1.

Figure 6a. Interrupt type 1, method 1

Figure 6b. Interrupt type 1, method 2

Figure 7a. Interrupt type 1, method 1 with disturbance (win-dow)

Both experiment in figure 6a and 6b have the same settlingtime, i.e. 9 minutes.

Figure 7b. Interrupt type 1, method 2 with disturbance (win-dow)

The experiment in figure 7a and 7b need 3.8 and 6 minutesto handle this disturbance for method 1 and 2.

Figure 8a. Interrupt type 1, method 1 with disturbance (dryice)

Figure 8a. Interrupt type 1, method 2 with disturbance (dryice)

The experiment in figure 8a and 8b need 4 and 5.7 minutesto handle this disturbance for method 1 and 2.Table 2. Summary of figure 6 to 8

Now the random delay is set more than one sampling time.This kind of interrupt will be called as interrupt type 2.

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Figure 9a. Interrupt type 2, method 1

The settling time for the experiment (9a and 9b) is 10 and 6minutes for method 1 and 2.

Figure 9b. Interrupt type 2, method 2

Experiment in figure 10a and 10b need 5.1 and 5.4 minutesto handle this disturbance for method 1 and 2.

Figure 10a. Interrupt type 2, method 1 with disturbance(window)

Figure 10b. Interrupt type 2, method 2 with disturbance(window)

Figure 11a. Interrupt type 2, method 1 with disturbance(dry ice)

Figure 11b. Interrupt type 2, method 2 with disturbance(dry ice)

handle this disturbance.Table 3. Summary of figure 9 to 11

IV. Discussion

Table 1 shows the summary of the experiments when theretheir responses are almost the same.These results make sense for the plant is categorized asslow response plant. The order of processing and actuat-ing processes has no effect. Beside the sampling time issmall enough compare to the dynamic behavior of the plant.The experiments involved simulated ISR (random delay)showed almost the same results. The disturbance signalby opening window made the experiment gave differentresult. This is due to the homogeneous air condition in-side the chamber.If the authors hoped for different result, then table 2 is notsatisfying. The system never looses its sampling time. Theperformance of the system is still good.

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V. Conclusions

From the experiments the authors conclude that the orderof the process in one sampling time has no effect for thisplant. This will be the same also for other slow responseplant. The sampling time used in this project is small enoughcompare to the dynamic behavior of the plant. It is inter-esting to use bigger sampling time because the 2nd methoduse actuated signal from the previous sampling.

With this result, it is interesting to repeat the experimentwith fast response plant like motor speed control. The mainpoint is to evaluate the performance of the system usingthe 2nd method.

The disturbance signals, i.e. window and dry ice give dif-ferent result. The authors recommended dry ice for this ismore stable than by opening the window. In order to havefaster response for the heating process, it is necessary tochange the bulbs with heater.

References

[1] Amerongen, J., T. J. A. De Vries. Digital Control Engi-neering. Faculty EE-Math-CS, Department of Elec-trical Engineering, Control Engineering ResearchGroup, University of Twente. 2003

[2] Gambier, A. “Real-time Control System: A Tutorial”. Pro-ceeding of 5th Asian Control Conference. Australia,2004

[3] Fujimoto, H and Y. Hori. “Advanced Digital MotionControl Based on Multirate Sampling Control”. Pro-ceeding of 15th Triennial World Congress of the In-ternational Federation of Automatic Control(IFAC), Barcelona, Spain, 2002.

[4] National Semiconductor. LM35 Datasheet. November2000. Accessed on March 17, 2006. <http://www.alldatasheet.com/pdf/8866/NSC/LM35.html>

[5] Adinegara Astra, Arief. “Kendali Rumah Jarak JauhMemanfaatkan Radio HT”. Tugas Akhir S1.Surabaya: Universitas Kristen Petra, 2004

[6] Fairchild Semiconductor, MOC3020 Datasheet. Ac-cessed on March 18, 2006 <http://w w w. a l l d a t a s h e e t . c o m / p d f / 2 7 2 3 5 / T I /MOC3020.html>

[7] Atmel Corporation, ATMega8 Datasheet. Oktober 2004.Accessed on March 15, 2006. <http://www.alldatasheet.com/pdf/80258/ATMEL/ATmega8-16PI.html>

[8] Ogata, K. Modern Control Engineering 4th ed. UpperSaddle River, NJ, 2002.

[9] MCS Electronics, BASCOM v1.11.7.8 User Manual.2005. April 25, 2006. <http://avrhelp.mcselec.com/bascom-avr.html>

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AbstractIn support of continuing prosperous evolution of information technology, statistic multivariate field of study has alwaysbeen one the fundamental platform needed in laying the principal foundation of the knowledge. Destination informationsystem implementation in various companies in general, namely to help improve performance and service to the customermore effective and efficient. Universities in the world, for example, to avoid errors and data redundancy, system informa-tion is used to form one Schedule Plan Study (JRS) students each semester. But it was not expected, because of problemswith regard to space and time conflicts occur everywhere. To anticipate, while the experiment is done using the conceptof permutation combination, which is the application of the science branch of multivariate statistics in IT. The concept isenough to help decrease the number of conflicts more than 50% of all conflicts. However, the concept is less effective andnot in accordance with their needs, can not be detected even earlier if the status of permanent conflicts. Method is knownas Auto Generated Probabilistic Combination (AGPC). AGPC is a method that combines sequence difference from theobjects that is to be done without repeating the object of every order, and the sequence criteria are based on the status ofconflicts, permutation for each item per-item conflict. In this article have also identified at least 4 issues in regard to thefundamental concept of permutation combination, defines the method Auto Generated Probabilistic Combination (AGPC)as a means of tackling the new conflicts, and that is the last AGPC in the SIS-OJRS in Raharja Universities. AGPC thismethod is very important to be developed especially in the process of JRS, because its function is to provide effectivewarning in the status of the conflicts that are permanent, and to make the process of re-permutation. It can be said thatthis method AGPC erase the operational work in approximately 80% of the original work and does not need to do tocancel the entire schedule conflicts manually.

Index Terms— JRS, Conflicts, Permutation Combination, AGPC

Saturday, August 8, 200915:10 - 15:30 Room L-212

Statistic multivariate using Auto Generated ProbabilisticCombination on Forming schedule study plan

I. IntroductionPreparation of Schedule Study Plan (JRS) is made as oneof the regular agenda that is run each semester in the uni-versity environment. Preparation activities that are con-ducted on all students to take a course or determine who istaken in each semester. The process is a complex schedulein determining whether the determination of the scheduleand schedule students to take enough lecturers and im-proves as the concentration of which is dominant for alltime at that time. In order to avoid errors and data redun-dancy, the University set a policy to use a system informa-

tion processing to make the process of schedule Plan Study(JRS). But not enough to run smoothly, condition becausethe data resulted in the system faced the problem that is aquite complex problem with regard to the conflicts. Ini-tially the problem is handled manually through a processto cancel, but in fact more and more time is not possible tore-do things that are manual. There is a concept that canbe seen that when the issue is conflicts. The concept isknown by the name of Permutation Combination. The con-cept was developed with the adopted principles of statis-

217

HidayatiSTMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

Untung RahardjaSTMIK RAHARJA

Raharja Enrichment Centre (REC)Tangerang - Banten, Republic of Indonesia

[email protected]

Suryo GuritnoIlmu Komputer

Universitas Gadjah MadaIndonesia

[email protected]

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tical knowledge about permutation and combination. Howit works is rearrange the order in which subjects had to betaken by a student for class schedule and then determinedbased on the sequence. For example, say a student to take4 subjects, namely subjects A, B, C, and D. If sorted for thefirst time into the ABCD, the subject is a determination asconflicts. For the first, subjects choose a schedule where,different from the next order of subjects, such as B, C, andD are tied to the course schedule on the previous order.Subject B, for example, need to find a schedule that doesnot conflicts with subjects A, and so on for subjects C andD. But the order will still be conflicts if it turns out as aclass devoted to subjects B only and accidental conflictswith the schedule A subject that has been taken. Basicallythe first priority of the main.

II. Platform Theory

A. JRSAccording to the administration of the PSMA GunadarmaUniversity, charging Card Plan Study is an activity under-taken by all students to take an active or determine thecourse taken in each semester. The script SOP Bogor Ag-riculture Institute (IPB), The plan is the process of studentstudy the determination of educational activities that willbe implemented in the semester the students will come.Teachings include eye, Subject, Training, Seminar, Prac-tice Field, KKN, Internship and Final Project Work.· KRS is Card Plan Study, lists the Eyes Teachingstudents to be taken in the semester to come (includingover semester).· KSM is a Student Study Card, containing a list ofMata Teachings taken in the semester the student is run-ning, is based on KRS.· In the script SOP UIN Sunan Kalijaga have aboutKPRS. KPRS Card is a study plan changes, the changesinclude the teaching of the selected students in KRS.

B. Permutationclopedia, permutation is a rearrangement of items in theing. If there is an alphabetic string abcd, then the stringcan be written back with a different sequence: acbd, dacb,and so on. Read more there are 24 ways to write a fourthletter in the order in which different each other.

abcd abdc acbd acdb adbc adcbbacd badc bcad bcda bdac bdcacabd cadb cbad cbda cdab cdba dabc dacb dbac dbca dcab dcba

Each strand contains a new element with the same originalstring abcd, just written with a different sequence. So eachnew thread that has a different sequence of the strand is

called the permutation of abcd.

1). A large measure may be a permutationTo make a permutation of abcd, can assumptive that thereare four cards with each letter, we want the bucket back.There are 4 empty boxes that we want to fill with each card:Card Empty Box ————— ——————— a b c d [ ] [ ] [ ] [ ]

So we can fill each box with the card. Of course, every cardthat has been used can not be used in two places at once.The process is described as follows:In the first, we have 4 options for the card is inserted.

Card Box ————— ——————— a b c d [ ] [ ] [ ] [ ] ^ 4 choice: a, b, c, d

Now, living conditions card 3, then we have 3 options tostay for the card is inserted in the second.

Card Box ————— ——————— a * c d [b] [ ] [ ] [ ] ^ 3 choice: a, c, d

Because two cards have been used, then for the third, wehave two options to stay.

Card Box ————— ——————— a * c * [b] [d] [ ] [ ] ^ 2 choice: a, cThe last one, we only have a choice.Car d Box ————— ——————— a * * * [b] [d] [c] [ ] ^ 1 choice: aLast condition of all boxes are filled.

Card Box ————— ——————— * * * * [b] [d] [c] [a]

In each step, we have a number of options that are re-duced. So the number of all possible permutation is 4 × 3 ×2 × 1 = 24 units. If the number of card 5, the same way thatthere can be 5 × 4 × 3 × 2 × 1 = 120 possibilities. So if thegeneralization, the number of permutation of n elements isn!.

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III. ProblemsScanning may permutation combination method can over-come these conflicts, has even succeeded in lowering thenumber of conflicts more than 50% of the original. Butafter that the result be not the maximum, it is not effectiveand not in accordance with their needs. Permutation com-bination method has a job that is sort objects into a se-quence that is not the same as the previous order. Thus,this method only reorder subjects, without looking at thenumber of classes devoted to each subject. Basically ifyou have 2 subjects, while the second course is open only1 class, schedule conflicts and coincidences, even if thepermutation is done how many times the results are cer-tainly conflicts. This is the reason why the method is saidpermutation combination is not effective. Because you cannot detect the fact that the earlier status conflicts are per-manent. Not only that, permutation method can be anycombination does not comply with the requirement duecourse sequence generated does not match that shouldbe. This means, if there is a student taking 4 subjects ABCD,and accidental conflicts in the 2 subjects, namely subjectsC and D. Then the permutation is done so that the order beBACD, whether the status of conflicts may be missing?The answer, most likely state conflicts will not be lost.Because that is clear is that conflicts subjects C and D,and the beginning of the permutation B subjects, the sub-jects are clearly B is not conflicts, so it does not need tochange a schedule. Now the problem is why this combina-tion permutation method does not comply with the saidrequirement, because this method is less sensitive to thestatus of conflicts.Based on the contention that there are 4 issues preferenceof making this article, namely:1. Such as whether the method can be effectively used to overcome conflicts?2. Such as whether the method can inform the status of

earlier conflicts that are permanent?3. Such as whether the method can be sensitive to the

status of conflicts and permutation for subjects whohave conflicts?

4. Such as whether the method can not only stop permutation to sort the subjects, but also sort by class openedper-course?

IV. TroubleshootingTo overcome the problems as described above, can bedone through the application of the method Auto Gener-ated Probabilistic Combination (AGPC). Here are 5 charac-teristics of the Auto Generated Probabilistic Combination(AGPC) that is applied in the process of handling conflictsSchedule Study Plan (JRS):1. Data required classes for each subject that are con

flicts.

2. There is a warning that if all subjects are conflicts thatcan only be opened one class, and the permutation isnot performed.

3. Permutation is done for the entire class for each courseare the conflicts, followed by a search and classes forother subjects that are not conflicts.

4. If that is not generated schedule conflicts, the permutation has been stopped.

5. If up to the permutation process was done all theschedule conflicts are still produced, all schedulesare displayed for selection can be done as well askeep the schedule replaces the old schedule, and beback by the permutation a schedule.

To be able to handle conflicts effectively, it needs an ex-amination to assess whether the initial status conflicts areconflicts that are permanent or not? If so, then the need ofwarning that the status of conflicts can not be removed,so that the process of permutation need not be done. Thisis no point [1] and [2]. Not only that, how to work withdifferent AGPC any permutation combination in general.AGPC precede the process of permutation are subject toconflicts with the first goal on the issue of the problem.This also makes AGPC become more effective. Permuta-tion process performed AGPC not permutation-based sub-jects, but is based on the permutation of classes in eachsubject. This is no point [3]. Here is an example:If there are a schedule conflicts. Schedule that consists of5 subjects that consisted of subjects A, B, C, D, and E.And there are 3 subjects that conflicts are the subjects A,B, and D. After review, the course opened a 2-class, namelyA1 and A2. Subjects B 1 opened the B1 class. While thecourse was opened C-class 2, namely C1 and C2. So thenumber of permutation for the three subjects was obtainedfrom the following formula:P = jumlah MK A x jumlah MK B x jumlah MK C = 2 x 1 x 2 = 4Order, namely:1. A1, B1, C12. A1, B1, C23. A2, B1, C14. A2, B1, C2

Then proceed with the determination of classes for othersubjects that are not conflicts. This would adjust the sched-ule on-schedule study previously. Generated when an or-der of the schedule conflicts that are not, although notcomplete permutation, the permutation process is stopped.And the system provides the option to schedule if youwant to be a fixed schedule replaces the previous sched-ule? This is the point of no explanation [4]. Like otherswith the process if the permutation is complete, the entireschedule of all conflicts are generated. This is no point [5].The system will display the entire schedule has been cre-ated. And users are given the option, if you want to select

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one of the schedule-the schedule, or would like to makethe process of re-permutation schedule.Using the methods of handling conflicts Auto GeneratedProbabilistic Combination (AGPC) has been implementedon the University Raharja, namely the information systemSIS OJRS (Online JRS). Students Information Services, orcommonly abbreviated SIS, is a system developed by Uni-versity Raharja for the purpose of the system of informa-tion services to students at an optimal. Development ofSIS is also an access to the publication Raharja Universi-ties in the field of computer science and IT world in par-ticular. SIS has been developed in several versions, eachof which is a continuation from the previous version ofSIS. SIS OJRS (Online Schedule Study Plan) is the versionto the SIS-4. Appropriate name, SIS OJRS made for theneeds of the student lecture, which is to prepare the JRS(Schedule Study Plan). The end result to be achieved fromthe SIS that is produced this OJRS JRS (Schedule StudyPlan) and KST (Study Cards Stay) the minimal number ofconflicts may even reach up to 0%. Therefore, to conflictswith the reduction can be an effective way, the method isapplied Auto Generated Probabilistic Combination (AGPC)this.

Figure 1. Study Cards Stay (KST) on the SIS OJRS

The above picture is the view KST students. From thispage you can know information about what the class ob-tained by a student this semester, what day, the room where,at the time, and what the status of its class, the class con-flicts or not. At the top of the KST there is also a text link“Auto_List_Repair”. Its function is to run this methodAGPC. If you find that the two subjects which are listed onthe conflicts over the KST subjects MT103 and PR183each opened only 1 class, then a warning will appear stat-ing that the status of permanent conflicts, so that the sta-tus of conflicts can not be eliminated and the permutationdoes not run . Next display warning.

Figure 2. Warning on the AGPC

But another case if one of the two subjects have openedmore than 1 class, then the permutation still running. How-ever, at the time when the process of permutation and thenproduced a stack schedule conflicts are not, then the per-mutation process is stopped. Then schedule the order ofdata displayed results is the process of permutation. Nextappearance.

Figure 3. Views Log AGPC order if found not to scheduleconflicts

The image is only produced 2 permutation process, wherethe permutation to-2 contains the order of the scheduleconflicts that do not. When click on the OK button, theschedule has been approved to replace the old scheduleKST student concerned. KST displayed below the OKbutton after a new executable.

Figure 4. Study Cards Stay (KST) on the SIS OJRS resultsAGPC

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There is one more condition on the AGPC this method atthe time when the process is complete permutation runs,but the order of the schedule generated conflicts are en-tirely fixed, the schedule-the schedule will also be displayed.Next appearance.

Figure 5. Views Log AGPC if not found the order of theschedule conflicts that are not

In the column “Ket” shown in the image [5] above, a writ-ten schedule that all are composed of “conflicts”. Whenthe status of conflicts is clicked, it means that those withschedule conflicts have been approved to replace the oldschedule KST students concerned. However, if you wantto repeat the method AGPC conflicts based on status inone of the regular schedule you have, simply click on thebutton with the “Repeat” is listed under the status “con-flicts”. Distance permutation then executed again.

A. DatabaseSIS OJRS implemented on the University Raharja data-base using SQL Server. In the database server is eitherintegrated various databases that the database used bythe Student Information Services (SIS), and Green Orches-tra (GO), Raharja Multimedia Edutainment (RME), etc.. SISOJRS use the same database as the database used by theStudent Information Services (SIS).This database is created on the table-table is needed re-garding the process AGPC. There are two types of tablesthat must be prepared, namely: a table that contains dataCT_List permutation classes for subjects who are con-flicts, and the table CT_List2 is a table that contains acombination of data permutation classes for subjects andthose with conflicts are not conflicts.

Figure 6. Structure of the table tbl CT_List

The table above is a table which is the main place wherethe data required to store the initial data permutation. Thesefields are required in compliance with the existing system.To Field, Kode_MK, NIM, No, and Jml_Kelas Class is afield that describes the data that must be met before theprocess of permutation actually executed. Jml_Kelas Thecontents of the field that determines whether the permuta-tion to be feasible or not. If feasible, then start the processof permutation process is executed and entered into thetable CT_List2.

Figure 7. Structure of the table tbl CT_List2

B. Listing Program

Check early

‘Delete data di CT_In_New1 dan CT_In_New2Sql=”Delete from CT_List where NIM=’”&trim(strnim)&”’”set rs=conn.execute(Sql)Sql2=”Delete from CT_List2 whereNIM=’”&trim(strnim)&”’”set rs2=conn.execute(Sql2)‘cari data mhsnyaSqla=”select * from sumber_mahasiswa whereNIM=’”&trim(strnim)&”’”set rsa=conn.execute(Sqla)‘Looping Kelas Bentrok‘seleksi kst yang bentrokSql3=”select * from CT_11_7_12_2 whereNIM=’”&trim(strnim)&”’ and Bentrok=1"set rs3=conn.execute(Sql3)Nom2=1while not rs3.eof‘seleksi jumlah kelas untuk kode_MK tersebutSql4=”select COUNT(Kelas) as jum1 FROM (select Kelasfrom CT_11_7_12_2 whereKode_MK=’”&trim(rs3(“Kode_MK”))&”’ andShift=”&rsa(“Shift”)&” and NIM is null GROUP BYKelas)DERIVEDTBL”set rs4=conn.execute(Sql4)‘seleksi kelas-kelas untuk kode_MK tersebutSql5=”select Distinct Kode_MK,Kelas FROMCT_11_7_12_2 whereKode_MK=’”&trim(rs3(“Kode_MK”))&”’ and

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Shift=”&rsa(“Shift”)&” and NIM is null GROUP BYKode_MK,Kelas ORDER BY Kode_MK,Kelas”set rs5=conn.execute(Sql5)Nom=1while not rs5.eof‘seleksi datanyaSql6=”select * from CT_List whereKode_MK=’”&trim(rs5(“Kode_MK”))&”’ andNIM=’”&trim(strnim)&”’ and Kelas=”&rs5(“Kelas”)&””set rs6=conn.execute(Sql6)‘jika blm ada datanyaIf rs6.eof then‘insert datanyaSql7=”Insert intoCT_List(Ke,Kode_MK,NIM,No,Kelas,Jml_Kelas)Values(“&Nom&”,’”&trim(rs5(“Kode_MK”))&”’,’”&trim(strnim)&”’,”&Nom2&”,”&rs5(“Kelas”)&”,”&rs4(“jum1”)&”)”set rs7=conn.execute(Sql7)Nom=Nom+1ElseNom=Nomend ifrs5.movenextwendNom2=Nom2+1rs3.movenextwend

The number of permutation

‘Jumlah loopingSql8=”select distinct Kode_MK,Jml_Kelas,NIM fromCT_List where NIM=’”&trim(strnim)&”’”set rs8=conn.execute(Sql8)Nom3=1Nom3a=1while not rs8.eofNom4=rs8(“Jml_Kelas”)Nom3=Nom3*Nom4Nom3a=Nom3a+1rs8.movenextwendNom3b=Nom3a-1

Warning

‘Jika jumlah kemungkinan looping hanya 1If Nom3=1 then<div align=”center”><p><font color=”#FF0000" size=”3"face=”tahoma”><strong>BENTROK TIDAK DAPATDIHILANGKAN </strong></font></p><p><strong><fontcolor=”#000000" size=”3" face=”tahoma”>&lt;&lt;— <ahref=”Tampil_Kelas_Shift4.asp?NIM=<%= strnim %>”target=”_self”>Back To KST</a></font></strong></p></

div>

Permutation Run

elseif Nom3 > 1 then‘jika = 2if Nom3b=2 thenNom5=1‘seleksi kelas yg No=1Sql9a=”select distinct * from CT_List whereNIM=’”&trim(strnim)&”’ and No=1 order by Ke”set rs9a=conn.execute(Sql9a)while not rs9a.eof‘seleksi kelas yg No=2Sql9b=”select distinct * from CT_List whereNIM=’”&trim(strnim)&”’ and No=2 order by Ke”set rs9b=conn.execute(Sql9b)while not rs9b.eof‘insert kode_mk untuk No=1Sql10a=”insert intoCT_List2(Ke,Kode_MK,Kelas,NIM,No,Jml_Kelas)values(“&Nom5&”,’”&trim(rs9a(“Kode_MK”))&”’,”&rs9a(“Kelas”)&”,’”&trim(strnim)&”’,”&rs9a(“No”)&”,”&rs9a(“Jml_Kelas”)&”)”set rs10a=conn.execute(Sql10a)‘insert kode_mk untuk No=2Sql10b=”insert intoCT_List2(Ke,Kode_MK,Kelas,NIM,No,Jml_Kelas)values(“&Nom5&”,’”&trim(rs9b(“Kode_MK”))&”’,”&rs9b(“Kelas”)&”,’”&trim(strnim)&”’,”&rs9b(“No”)&”,”&rs9b(“Jml_Kelas”)&”)”set rs10b=conn.execute(Sql10b)‘Insert MK yg lainnyaSql11=”select distinct Kode_MK from CT_11_7_12_2where NIM=’”&trim(strnim)&”’ and Bentrok=0"set rs11=conn.execute(Sql11)Nom6=3while not rs11.eof‘cari jumlah kelas yg dibuka untuk kode_mk tersebut ygtidak penuhSql12=”select COUNT(Kelas) as jum2 FROM (select TOP100 PERCENT Kelas from CT_11_7_12_2 where(Kode_MK=’”&trim(rs11(“Kode_MK”))&”’ and NIM isnull) GROUP BY Kelas ORDER BY Kelas) DERIVEDTBL”set rs12=conn.execute(Sql12)‘insert kode_mk nyaSql13=”Insert intoCT_Lis t2 (Ke ,Kode_MK,NIM,No , Jml_Ke las )Values(“&Nom5&”,’”&trim(rs11(“Kode_MK”))&”’,’”&trim(strnim)&”’,”&Nom6&”,”&rs12(“jum2”)&”)”set rs13=conn.execute(Sql13)Nom6=Nom6+1rs11.movenextwend‘cari kstnya‘cari data mhsnyaSql14=”select * from sumber_mahasiswa whereNIM=’”&trim(strnim)&”’”set rs14=conn.execute(Sql14)

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‘cari urutan listnyaSql15=”select * from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=”&Nom5&” Order ByNo”set rs15=conn.execute(Sql15)while not rs15.eofIf isnull(rs15(“Kelas”)) then‘cari di view, kelas yg ga bentrokSql16=”select * from View_CT_11_7_12_2 where NIM isNull and Kode_MK=’”&trim(rs15(“Kode_MK”))&”’ andShift=”&rs14(“Shift”)&” and (Kode_Waktu Not in (selectKode_Waktu from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=”&Nom5&” andKode_Waktu is not null)) and (Kode_Waktu2 Not in (se-lect Kode_Waktu from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=”&Nom5&” andKode_Waktu is not null)) and (Kode_Waktu Not in (selectKode_Waktu2 from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=”&Nom5&” andKode_Waktu2 is not null)) and (Kode_Waktu2 Not in (se-lect Kode_Waktu2 from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=”&Nom5&” andKode_Waktu2 is not null)) order by No”set rs16=conn.execute(Sql16)‘jika ketemu kelas yg tidak bentrokIf not rs16.eof then‘update kelasnyaSql17=”update CT_List2 setKelas=”&rs16(“Kelas”)&”,Kode_Waktu=’”&trim(rs16(“Kode_Waktu”))&”’,Kode_Waktu2=’”&trim(rs16(“Kode_Waktu2”))&”’,Bentrok=0where NIM=’”&trim(rs15(“NIM”))&”’ andKe=”&rs15(“Ke”)&” andKode_MK=’”&trim(rs15(“Kode_MK”))&”’ andNo=”&rs15(“No”)&””set rs17=conn.execute(Sql17)’jika ga ketemu kelas yg tidak bentrokelseif rs16.eof then‘cari kelas apa sajaSql17=”select * from View_CT_11_7_12_2 where NIM isNull and Kode_MK=’”&trim(rs15(“Kode_MK”))&”’ andshift=”&rs14(“Shift”)&” order by No”set rs17=conn.execute(Sql17)‘update kelasnyaSql18=”update CT_List2 setKelas=”&rs17(“Kelas”)&”,Kode_Waktu=’”&trim(rs17(“Kode_Waktu”))&”’,Kode_Waktu2=’”&trim(rs17(“Kode_Waktu2”))&”’,Bentrok=1where NIM=’”&trim(rs15(“NIM”))&”’ andKe=”&rs15(“Ke”)&” andKode_MK=’”&trim(rs15(“Kode_MK”))&”’ andNo=”&rs15(“No”)&””set rs18=conn.execute(Sql18)end ifelse‘cari di view, kelas yg ga bentrokSql16=”select * from View_CT_11_7_12_2 where NIM isNull and Kode_MK=’”&trim(rs15(“Kode_MK”))&”’ and

Shift=”&rs14(“Shift”)&” and Kelas=”&rs15(“Kelas”)&”and (Kode_Waktu Not in (select Kode_Waktu fromCT_List2 where NIM=’”&trim(strnim)&”’ andKe=’”&Nom5&”’ and Kode_Waktu is not null)) and(Kode_Waktu2 Not in (select Kode_Waktu from CT_List2where NIM=’”&trim(strnim)&”’ and Ke=’”&Nom5&”’ andKode_Waktu is not null)) and (Kode_Waktu Not in (selectKode_Waktu2 from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=’”&Nom5&”’ andKode_Waktu2 is not null)) and (Kode_Waktu2 Not in (se-lect Kode_Waktu2 from CT_List2 whereNIM=’”&trim(strnim)&”’ and Ke=’”&Nom5&”’ andKode_Waktu2 is not null)) order by No”set rs16=conn.execute(Sql16)‘jika ketemu kelas yg tidak bentrokIf not rs16.eof then‘update kelasnyaSql17=”update CT_List2 setKelas=”&rs16(“Kelas”)&”,Kode_Waktu=’”&trim(rs16(“Kode_Waktu”))&”’,Kode_Waktu2=’”&trim(rs16(“Kode_Waktu2”))&”’,Bentrok=0where NIM=’”&trim(rs15(“NIM”))&”’ andKe=”&rs15(“Ke”)&” andKode_MK=’”&trim(rs15(“Kode_MK”))&”’ andNo=”&rs15(“No”)&””set rs17=conn.execute(Sql17)’jika ga ketemu kelas yg tidak bentrokelseif rs16.eof thenSql16a=”select * from View_CT_11_7_12_2 where NIM isNull and Kode_MK=’”&trim(rs15(“Kode_MK”))&”’ andKelas=”&rs15(“Kelas”)&” order by No”set rs16a=conn.execute(Sql16a)‘update kelasnyaSql17=”update CT_List2 setKode_Waktu=’”&trim(rs16a(“Kode_Waktu”))&”’,Kode_Waktu2=’”&trim(rs16a(“Kode_Waktu2”))&”’,Bentrok=1where NIM=’”&trim(rs15(“NIM”))&”’ andKe=”&rs15(“Ke”)&” andKode_MK=’”&trim(rs15(“Kode_MK”))&”’ andNo=”&rs15(“No”)&””set rs17=conn.execute(Sql17)end ifend if‘cari bentroknyaSql19=”select sum(Bentrok) as jum_bentrok from CT_List2where NIM=’”&trim(rs15(“NIM”))&”’ andKe=”&rs15(“Ke”)&””set rs19=conn.execute(sql19)Ke_brp=rs15(“Ke”)If rs19(“jum_bentrok”) <> 0 thenSql20=”select * from CT_List2 whereNIM=’”&trim(rs15(“NIM”))&”’ and Ke=”&rs15(“Ke”)&”and Bentrok=1"set rs20=conn.execute(Sql20)while not rs20.eofSql21=”select * from CT_List2 whereNIM=’”&trim(rs20(“NIM”))&”’ and Ke=”&rs20(“Ke”)&”

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and Kode_Waktu=’”&trim(rs20(“Kode_Waktu”))&”’ andBentrok=0 andKode_MK<>’”&trim(rs20(“Kode_MK”))&”’ orNIM=’”&trim(rs20(“NIM”))&”’ and Ke=”&rs20(“Ke”)&”and Kode_Waktu2=’”&trim(rs20(“Kode_Waktu2”))&”’and Bentrok=0 andKode_MK<>’”&trim(rs20(“Kode_MK”))&”’ orNIM=’”&trim(rs20(“NIM”))&”’ and Ke=”&rs20(“Ke”)&”and Kode_Waktu=’”&trim(rs20(“Kode_Waktu2”))&”’ andBentrok=0 andKode_MK<>’”&trim(rs20(“Kode_MK”))&”’ orNIM=’”&trim(rs20(“NIM”))&”’ and Ke=”&rs20(“Ke”)&”and Kode_Waktu2=’”&trim(rs20(“Kode_Waktu”))&”’ andBentrok=0 andKode_MK<>’”&trim(rs20(“Kode_MK”))&”’”set rs21=conn.execute(Sql21)while not rs21.eofSql22=”update CT_List2 set Bentrok=1 whereNIM=’”&trim(rs21(“NIM”))&”’ and Ke=”&rs21(“Ke”)&”and Kode_MK=’”&trim(rs21(“Kode_MK”))&”’”set rs22=conn.execute(Sql22)rs21.movenextwendrs20.movenextwendend ifrs15.movenextwendSql23a=”select sum(Bentrok) as jum_bentrok2 fromCT_List2 where NIM=’”&trim(strnim)&”’ andKe=”&Ke_brp&””set rs23a=conn.execute(Sql23a)If rs23a(“jum_bentrok2”) = 0 thenresponse.redirect(“Tampil_CT_List2.asp?rsnim=”&strnim)end ifNom5=Nom5+1rs9b.movenextwendrs9a.movenext

wendresponse.redirect(“Tampil_CT_List2.asp?rsnim=”&strnim)

V. ConclusionAuto Generated Probabilistic Combination (AGPC) is animportant part in the process of schedule Plan Study (JRS).How it works is sensitive to the status of conflicts basedon the permutation AGPC make this exactly right to handleconflicts core problem. In addition AGPC can provide earlywarning status if the conflicts are permanent, so that thepermutation does not need to be done. This is very helpfulbecause in addition to saving hard drive capacity, AGPCalso provide a clear status of the schedule can not be loststatus conflicts.

References

[1]Andi (2005). Aplikasi Web Database ASP MenggunakanDreamweaver MX 2004. Yogyakarta: Andi Offset.

[2]Bernard, R, Suteja (2006). Membuat Aplikasi WebInteraktif Dengan ASP. Bandung: Informatika.

[3]Untung Rahardja (2007). Pengembangan StudentsInformationServices di Lingkungan Perguruan TinggiRaharja. Laporan Pertanggung Jawaban. Tangerang:Perguruan Tinggi Raharja.

[4]Anonim (2009). Standar Operating Procedure (SOP) In-stitute Pertanian Bogor.

[5]Anonim (2009). Standar Operating Procedure (SOP)Penyusunan KRS dan KPRS UIN Sunan Kalijaga.

[6]Santoso (2009). Materi I : Permutasi dan Kombinasi.Diakses pada tanggal 5 Mei 2009 dari : http://ssantoso.blogspot.com/2009/03/materi-i-permutasi-dan-kombinasi.html

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Information System Department – Faculty of Computer Study STMIK RaharjaJl. Jenderal Sudirman No. 40, Tangerang 15117 Indonesia

email: [email protected]

ABSTRACTDignity Pembangunan Jaya school that bernaungan with Dignity Education Foundation, in carry on its businessprocess doesn’t take down from IT’s support. That IT’s purpose especially in back up service to student / schoolgirl andoldster. So far was become sumberdaya IT’s change supporting. Changing that generally is mark sense commutation orapplication Pembangunan Jaya, changing supporting infrastructure, etc.. This changed effort is meant to be able to moreget IT’s point, so gets to back up service process eminently. Research that worked by it does analisis to set brings off IT anddevelops solution proposal get bearing by set to bring off IT, by use of default COBIT (Control Objectives Information andrelated Technolgy), with emphasis on domain deliver and support (DS), one that constitutes forwarding needful servicesooth. This research is deep does data collecting, with pass through kuesioner’s purpose to get data about system whichwalks and interview be utilized to know next expectation.

Key word: Manner brings off IT, COBIT

Saturday, 8 August 200915:10 - 15:30 Room M-AULA

EVALUATION SETS TO BRING OFF INFORMATION TECHNOLOGYBASES COBIT’S FRAMEWORK PEMBANGUNAN JAYA SCHOOL

CASE STUDYDina Fitria Murad, Mohammad Irsan

1. BACKGROUNDInformation technology (IT) presto amends, and it givesits exploit opportunity. That developing gets to give op-portunity will innovate product or new service gets IT’ssupport basis, so gets to make firm more amends or lastregular. IT’s purpose to back up firm business process,slated that happening process appropriate one is expected.But such, IT’s purpose not only wreaks benefit, but canalso evoke jeopardy. In result qualified information ser-vice, information adjusment of technology (IT) constitut-ing absolute requirement needful. In a general way imple-ment IT will be espoused a cost requirement consequencethat tingi, well of procurement facet hardware ,Pembangunan Jaya software , implementation and systempreserve as a whole. That thing did by expectation gets tobe reached its strategical and IT’s strategy already beendefined in particular and plan and firm business strategyas a whole.To the effect firm will be reached if IT Diimplementasikan’splanning and strategy ala in harmony with planning and

organization business strategy already been defined. IT’simplement that in harmony with that institution aim justgets resultant if backed up by manner system brings offIT( IT Governance ) one that good since planning phase,implementation and evaluation. Manner brings off IT con-stitutes integrated part on corporate management, rang-ing leadership and structure and processes inorganisational one ensures that IT organization backs upstrategy and organization target as a whole. Mark sensemanner implement brings off expected IT gets to give a lotof benefit, for example:1. Menguragi is jeopardy2. Harmonising IT with objective business3. Strengthening IT as unit of main business4. Transparent more business operation5. Increasing effectiveness and efficiencyDignity Pembangunan Jaya school that bernaungdibawahDignity Education Foundation, in carry on its businessprocess doesn’t take down from IT’s support. That IT’s

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purpose especially in back up service to student / school-girl and oldster. So far was become sumberdaya IT’s changesupporting. Changing that generally is mark sense com-mutation or application Pembangunan Jaya, changing sup-porting infrastructure, and doesn’t mark sense audit IT.This changed effort is meant to be able to more get IT’spoint, so gets to back up service process eminently. Infra-structure hardware and also software one that availableat all unit which is PC (workstation), server, printer etc.,generally difungsikan to carry on application that propknockabout operational.That implementation sets to bring off IT happens effec-tive, organization needs to assess insofar which sets tobring off IT that present happens and identify step-upwho can be done. That thing is prevailing on all processwhich needs to be brought off that consists in IT andmanner process brings off IT Is alone. Model purposematurity (maturity) in this case will make easy estimationby pragmatic approaching most structure to easy scaleapprehended and consistent.

2. BASIS FOR THEORY

2.1 Information system are an activity of procedure thoseare organized, if dieksekusi will provide information to backup decision making and operation in organisational (HenryC. Lucas in [JOGIYANTO 2003] 14). An old hand at othernames that information system is at deep an organizationthat bridge transactions processing requirement daily, back-ing up operation and provides particular reporting to onone’s side extern which need( [ROBERT 2003] 6 ).But Information System can also be defined as one formalprocedure series whereabouts gathered data, processedas information and is distributed to wearing. [HALL 2001]2.2 COBIT (Control Objectives Informatioan andRelated Technology) used to mean as intent as operationfor information and technology concerning, COBIT isdikenalkan’s first time on year 1996 one constitutes tools(tool) one that is made ready to manage information tech-nology (IT Governance Tool). COBIT was developed asone common application and was accepted as default whichwell for operation practice and kemananan TI.2.3 Definition about manner brings off IT one that istaken from IT Governance Institute are as follows: Man-ner brings off IT is defined as responsibility of executiveand board of directors, and consisting leadership, or-ganization chart and process that ensures IT firm backsup and expand objective and organization strategy. [IGI2005]To the effect manner brings off IT is to be able to lead IT’seffort, so ensures performa IT according to following ob-jective accomplishment [IGI 2003]a) IT in harmony with firm and promised gain realization.

b) IT’s purpose enables firm to exploit opportunity andmaximizes benefit.c) IT’s resource purpose that responsible.d) Management in point will jeopardy what do relate IT.

Framework to set brings off IT who is pointed out as itwere on following image, figuring manner process bringsoff that begins with determination objective IT firm, onethat give startup instruction. One series of IT’s activitythat is done, then done by measurement.

Framework’s I. image sets to bring off IT [IGI 2003]

Measurement result is weighed with objective, one thatwill get to regard instruction already being given on IT’sactivity and needful objective change [IGI 2003]

COBIT integrates good practice to IT and providing frame-work to set brings off IT, one that gets to help grasp andjeopardy management and gets gain that gets bearing withIT. COBIT’S implementation thus as framework mannerbrings off IT will get to give gain [IGI 2005]a) The better harmonization, up on business focus.b) One view, can be understood by management about

thing which done by IT.c) Responsibility and clear ownership is gone upon on

orientation processes.d) Can be accepted in common with third party and order

maker.e) Understanding share between the interested parties,

gone upon on one common language.

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f) Accomplishment is COSO the need( Committee ofSponsoring Organisations of the TreadwayCommision )to environmentally conducts IT.

In understands framework COBIT, need acknowledgedabout main characteristic where framework COBIT is made,and principle that constitutes it. There is characteristiceven main framework COBIT is business focused, processoriented, controls based and measurement driven , mean-while principle that constitutes it is [IGI 2005]“to gives utilised organization the information requiredreach its objective, organization needs to bring off andrestrains sumberdaya IT by use of process horde that moststructure to give the information required service.”

Academic concept“That studying is agreeable “ all superior programs to bepacked and led by that aim educative student to be moti-vated, are ardour and like teaching and learning process,feeling convenience and pleasingly in follows study, sothey can absorb knowledge more a lot of and get to de-velop all ability potency that its proprietary as optimal asmaybe.Curriculum and superior Program at presentasikan by ac-centuates effectiveness and kedinamisan what does fol-low protege developing. In its process, teaching and learn-ing activity leads protege to become likes studying, inde-pendent, creative deep faces and look for solution a prob-lem.

Protege thus is expected gets to do and finds quality andeffloresce innovations optimal alae.

II.Team Work’s image Managerial

3. STUDY

So far to evaluate manner brings off IT who is engagedhardware and software exploit at Schooled DignityPembangunan Jaya was done precisely, for it to need markssense measurement to that default. Default that is utilizedis COBIT (Control Objectives Informatioan and RelatedTechnology).Respondent determination to be adjusted by IT’s servicein its bearing with service on user. Respondent in thiscase will involve indigenous respondent a part or logisticIT and structural management. Respondent involvementthat originates function upon because function TI acts asprovider of IT’s service, meanwhile another function asuser of service.Respondent is agglomerated bases IT’s function and nonIT. IT’s respondent is differentiated group up senior andstaff group. IT’s group intended senior is range structuralmanagement and functional pro at IT. IT’s group staff herefathoms a meaning is IT’s staff that gets bearing face toface with performing services IT that current happens.Meanwhile group non IT, ranging structural managementat deep Foundation job unit Dignity Education.This respondent agglomeration is meant for gets to getview that adequately medley da helps in do IT’s processelect its following. There is amount even respondentwholly being pointed out as it were on III. table 1 hereun-der.

Respondent TotalTI is Staff Non IT 6 4Total 10Respondent i. table on elect processes

This indentifikasi’s performing will result elected process,and it constitutes legiatan or elect activity processes,where in aktvitas this will choose IT’s processes from do-main DS

3.1 Sample Elect methodTech snow ball sampling which is data collecting which isbegun of some bodies that criterion pock to be made sub-ject, be next that subject as information source about menwhich can make sample.

3.2 Data Collecting methodElect activity processes IT of IT’s processes that exists indomain DS who is done, gone upon on behalf zoom pro-cesses. Information hits to increase this behalf, gottenfrom party concerning. Utilised back up that informationacquisition, done by downloading by use of kuesioner.kuesioner that developed this was gone upon on Man-

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agement Awareness diagnostic [IGI 2000]According to the information required, kuesioner is em-phasized on behalfs level estimation. Then, to help respon-dent in understand process who will assess its behalf zoom,each process on kuesioner that was espoused by processdescription, one that brief process aim, as it were TI’s pro-cess description in COBIT [IGI 2005] .

3.3 InstrumentationTI’s processes that will do elect and estimation is processIT in domain DS. Domain this hits forwarding needful ser-vice reality, one that ranges forwarding service, securityand continuity management, support services on user, andmanagement on data and operational facility.poses IT’s process that ranges in domain DS comprise of:1) DS1 Define and Manage service Levels2) DS2 Manage is services’s Pihak Ketiga3) DS3 Manage Performance and Capacity4) DS4 Ensure Continuous service5) DS5 Ensure Systems security6) DS6 Identify and Allocate Costs7) DS7 Educate and Train Users8) DS8 Manage service Desk and Incidents9) DS9 Manage the Configuration10) DS10 Manage Problems11) DS11 Manage is Data12) DS12 Manage the Physical Environment13) DS13 Manage Operations3.4 Analisis’s tech Data

Utilised backs up analisis to determine process who willchoose to do following things:a. Accounting estimations selection percentage base to

increase its behalf for each process IT.b. Base behalfs level percentage that will do identifica

tion to know in as much as which increases requirement will process that.

c. Base acquired information above, will do pengkajianmore for elect need processes, and to the thing of doneby percentage count each TI’s process another.

To know in as much as which increases requirement willIT’s processes, can be seen on following table where eachestimation have mean as follows:a. 1: really insignificantb. 2: inessentialc. 3: little bit essentiald. 4: essentiale. 5: momentously

Code Process Requirement On Processes(%)1 2 3 4 5

TotalDS1 Define and Manage service Levels

DS2 Manage is services’s Pihak Ketiga DS3 Manage Performance and Capacity DS4 Ensure Continuous service DS5 Ensure Systems security DS6 Indetify and Allocate Costs DS7 Educate and Train Users DS8 Manage service Desk and Incidents DS9 Manage the Configuration DS10 Manage Problems DS11 Manage is Data DS12 Manage the Physical Environment DS13 Manage Operations

Table II.. The need percentage on process

After been done penelusuran to process the need is be-gun from DS1 until with DS13. III. table 4 succeeding stageswhich is determine rating percentage process, if anythingappreciative same presentase, in this case is gone uponon IT’s respondent view senior and Non IT just, sinceenough represents from provider flank service and IT’sservice user

Code Process Requirement On Processes(%)IT Non IT TotalSenior Staff

DS1 Define and Manage service Levels DS2 Manage is services’s Pihak Ketiga DS3 Manage Performance and Capacity DS4 Ensure Continuous service DS5 Ensure Systems security DS6 Indetify and Allocate Costs DS7 Educate and Train Users DS8 Manage service Desk and Incidents DS9 Manage the Configuration

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DS10 Manage Problems DS11 Manage is Data DS12 Manage the Physical Environment DS13 Manage Operations III. table. Rating percentage processes

3.4. 1 Management AwarenessManagement Awareness need to be done to know viewand organizer expectation, user and management party topeyedia services and pemngguna services IT.

3.4. 2 Level measurement Maturity ProcessBase identification result upon, therefore succeeding stageis do zoom measurement maturity (maturity) that process.This pengukurau’s activity over and above will result esti-mation about currently condition, will also result estima-tion about condition which is expected.On estimation activity processes, its estimation object isprocess to be chosen and its result is estimation increasematurity condition of currently and condition of that ex-pected, and repair target that will be done. It at picture as itwere is pointed out on following:

III. image. Estimation processes IT

Utilised up to means process estimation, done by down-loading. It is done by use of interview. That developedinterview is gone upon on things as follows:(1) Model maturity to process IT that.(2) Maturity attribute table .It is regarded for kuesioner’s Pembangunan Jaya that isattributed to make easy respondent in does estimation.Base judgment upon, resulting interview can be seen as itwere is pointed out on Attachment B.Respondent that is involved for interview inlay especiallyis respondent on IT’s function or part, one that its dailyruns face to face, and knows problem that gets bearing byprocess to be chosen. To back up analisis, acquired datafrom kuesioner, will at o and is done:(1) Doing count average to each attribut stuffing of allrespondent, well for estimation condition of currently andalso condition of that expected.(2) Level estimation maturity that process diperolah byundertaking count average all attribute, well for conditionnow and also condition of that expected.(3) Representasi is condition second each attribute pro-cesses that deep shaped diagram.(4) Remedial objective identification process is chosenthat will be done

Maturity’s attribute Requirement On Processes(%)now one that is expected

AC Awareness and Communication PSP Policies, Standards and Procedures TA Tools and Automation ONE Skills and Exepertise RA Responsibility and Accountability GSM setting and Measurement’s field goal Averagely Totaled

Table IV.. Measurement result increases maturity on DS ischosen

4. ANALISIS AND INTERPRETATION

4.1 Technological Management Condition analysisPembangunan Jayas Schooled Information DignityTo know condition of Schooled information technologymanagement Dignity Pembangunan Jaya is done someanalisis what do consisting of:a. Analisis domiciles TI’s functionb. Analisis management awarenessc. Analisi increases maturityExplanation and result of each analisis is described in thisfollowing explanation

4.2 Analisis domiciles IT’s FunctionInformations Technological unit get to lift full answer tobring off information technology as a whole (initiation,planning, implementation, monitoring and control).

4.3 Analisis Management AwarenessIdentification management awareness done by proposeskuisioner management awareness to all unit. kuisioner’sform management awareness that gets to be seen on At-tachment A. kuisioner management awareness’s respon-dent List can be seen on this following Table.

NO. Respondent Total1 SPJ’S principal 12 Spv IT 13 IT is TK’s Unit 14 TU is TK’s unit 15 IT is SD’s Unit 16 TU is SD’s Unit 17 IT is SMP’S Unit 18 TU is SMP’S Unit19 IT is SMA’S Unit 110 TU is SMA’S Unit 1 TOTAL 10

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Table V. Respondent kuisioner

Total kuesioner that is broadcast as much 10 sheets bytotals returns as much 10 sheets. The need scale that isutilized in kuisioner management awareness differenti-ated as 5 levels, beginning of “ so inessential “, “inessen-tial “, “little bit essential “, “essential “, and “ momentous“. rekapan kuisioner’s result management awarenessberdaasarkan increases requirement to process gets to beseen on this following table:

Code Process How important that process for aim tocarry on business

really insignificant inessentiallittle bit essential essentialmomentously

1 2 3 4 5DS1 Define and brings off service zoom 0%0% 0% 30% 70%DS2 Bringing off third party service 0%0% 0% 20% 80%DS3 Bringing off Performance and capacity0% 0% 10% 20% 70%DS4 Ensuring sustainable service 0%10% 10% 20% 60%DS5 Ensuring system security 0% 0%0% 30% 70%DS6 Doing identification and cost allocation0% 0% 0% 20% 80%

DS7 Teach and coaches user 0% 10%0% 20% 70%DS8 Adjoin and gives tips to user 0%10% 0% 30% 60%DS9 Bringing off configuration 0% 0%0% 40% 60%DS10 Bring off about problem and incident0% 0% 0% 30% 70%DS11 Bringing off data 0% 0% 0%30% 70%DS12 Bringing off facility 0% 0%0% 40% 60%DS13 Bringing off operation 0% 0%0% 20% 80%

Table VI. kuisioner’s recapitulation result managementawareness base requirement zoom to process

kuisioner’s recapitulation result on Table upon can be sim-plified by merges requirement zoom “ So not necessarily “,“Not necessarily “ and “ Applicable “ as requirement zoom“ Not Necessarily “, and merges requirement zoom “ Need“ and “ Really Need “ as requirement zoom “ Need “.Moderation result increases that requirement ditun jukkanon this following Table:Code Process

inessential essentialDS1 Define and brings off service zoom 0%100%DS2 Bringing off third party service 0%100%DS3 Bringing off Performance and capacity10% 90%DS4 Ensuring sustainable service 20%80%DS5 Ensuring system’s security 0%100%DS6 Doing identification and cost allocation0% 100%DS7 Teach and coaches user 10% 90%DS8 Adjoin and gives tips to user 10%90%DS9 Bringing off configuration 0% 100%DS10 Bring off about problem and incident0% 100%DS11 Bringing off data 0% 100%DS12 Bringing off facility 0% 100%DS13 Bringing off operation 0% 100%

Table VII. Recapitulation moderation result kuisioner man-agement awareness base requirement zoom to process

Graphic appearance of yielding recapitulation management

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awareness base requirement zoom to TI’s processes Dig-nity Pembangunan Jaya Schools can be seen on this fol-lowing image

Image IV. Skin graphic yielding kuisioner’s recapitulationmanagement awareness base requirement zoom to pro-cess

kuisioner’s recapitulation result management awarenesson table upon is analysed more by assumes that propri-etary process has presentase greatering to constitute im-portant or insignificant process available deep TI’s man-agement process. Process that needs there is inpengeloalan IT’s model Dignity Pembangunan Jaya Schoolcan be seen on table this following:Code Process Inessential EssentialDS1 Define and brings off service zoom vDS2 Bringing off third party service vDS3 Bringing off Performance and capacity vDS4 Ensuring sustainable service vDS5 Ensuring system security vDS6 Doing identification and cost allocation vDS7 Teach and coaches user vDS8 Adjoin and gives tips to user vDS9 Bringing off configuration vDS10 Bring off about problem and incident vDS11 Bringing off data vDS12 Bringing off facility vDS13 Bringing off operation v

VIII table. Process who shall there is in TI’s managementmodel Dignity Pembangunan Jaya School4.4 Maturities Level analysisMaturities level analysis be done by undertaking maturityzoom estimation that mangacu on model COBIT’S matu-rity Management Guidelines. COBIT’S maturity modelhas 6 level process TI, for example:a. 0 -Non Existent , management process be notbeen applied.b. 1 -Ad Hoc Initial /, management process doneby ala not periodic and not organized.c. 2 - Repeatable , process was done by ala repeti-tive.

d. 3 -Defined Process, process have most docu-mentation and documents, observation and alae uncom-mitted reporting periodic.e. 4 -Managed and Measurable , process mostkeeps company and be measured.f. 5 -Optimized, best practice , was applied deepmanagement process.IT’s processes whatever available is evaluated by use ofmaturity model then as compared to maturity zoom targetsthat concluded of vision, target and interview result, there-fore gets to be concluded that for gets to back up Schooledaim attainment Dignity Pembangunan Jaya at least matu-rity zoom that is done has available on zoom 4( Managedand Measurable).Base interview result with respondent, gotten by answerand statement those are proposed while do measurementinterview increases maturity can be seen on attachment B.Level maturity already most ranging identification on levelmaturity 1 (Ad Hoc Initial /) until 4( Managed and Mea-surable). Estimation result increases kamatangan that canbe seen on Table IV. 5 its followings:

Code TI’s process Maturity zoomDS1 Define and brings off service zoom 4DS2 Bringing off third party service 4DS3 Bringing off Performance and capacity 3DS4 Ensuring sustainable service 2DS5 Ensuring system security 3DS6 Doing identification and cost allocation 3DS7 Teach and coaches user 4DS8 Adjoin and gives tips to user 2DS9 Bringing off configuration 2DS10 Bring off about problem and incident 3DS11 Bringing off data 2DS12 Bringing off facility 3DS13 Bringing off operation 1

VIII table. Estimation result increases kamatangan

Base interview result and finding result that as opinion /opinion of respondent was gotten to usufruct maturityzoom measurement that is pointed out on table that..

4.5 RecommendationRecommendation application to settle maturity zoom gapdirected to by step who shall be passed through in achiev-ing expected maturity zoom. Recommendation applicationincreases maturity to process TI that has to increase matu-rity 1 will be led for attainment makes towards to increasematurity 2, then is drawned out to increase maturity 3, andin the end making for maturity zoom 4, such too its thingfor process what do have to increase another maturities.Recommendation to settle maturity zoom gap on processes

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IT’s managements Dignity Pembangunan Jaya Schools canthru do this following activity.

1. DS3 brings off performance and capacityRecommendation to make towards maturity zoom 4 - Man-aged and Measurable :

a. Arranged by corporate internal forum for gets to lookfor solution with upstairs about problem which arisesdeep performance and capacity management.

b. Acquisition process and software to measure performance and system capacity and compares it by increases service already be defined.

c. Peripheral automation to keep company sumberdayaspecific as disk of storage, network server and network gateways .

d. Update forte requirement routinely to all data management process to get membership and certification.

e. Under one’s belt formal training for staff what do relatewith performance and capacity management corresponds to plan and doing sharing science and followed by evaluation to trainings strategicaleffectiveness.

2. DS4 Ensures to service sustainableRecommendation to make towards maturity zoom 3 De-fined Process:

a. It does communication hit sustainable service requirement consistently.

b. Strategical documentation that is gone upon on system behalf and business impact.

c. It does periodic’s reporting hit sustainable serviceexamination.

d. It utilizes component that have tall accessibility and beapplied redundansi system.

e. Inventaris is system and main component looked afterby tights ala.

f. Individual follows service default and accept training.g. Its did pendefinisian and accountability establishment

for planning and continual examination.h. Its established intent and measurement in ensure con

tinual service and concerned by business aim.i. It does measurement and process observation.

3. Its applied IT balanced scorecard in main perfor-mance measurement.Recommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Arranged one procedure to ensure that continual service whole ala was understood and needful action wasaccepted widely at organization.

b. Data that most structured about service shall be gotten, analysed, reported, and is applied.

c. Under one’s belt training is provided for processessustainable service.

d. Applied by accountability and default for servicessustainable.

e. Changing business field, result of continual serviceexamination, and result of sustainable serviceexamination and performing internal best is regardeddeep care activity.

f. ketidaksinambungan’s incident services to be clasifiedand step-up aim for each acknowledged incident by allthe interesting party.

4. DS5 Ensures system securityRecommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Policy and security performing proveded with by specific security basic.

b. Analisis is impact and TI’s security jeopardy is doneconsistently.

c. Examination to trouble constitutes to process defaultand terformalisasi what does wend on step-up.

d. Security process coordinations IT goes to allorganisational security functions.

e. Standarisasi to identify, autentifikasi and user authorization.

f. analisis’s exploit cost / implemented supportivebenefit size security.

g. Done by security staff certification.h. Responsibility for the security IT is established clearly,

brought off and is applied.i. IT’s security reporting linked by business aim.

5. DS6 Does to identify and cost allocationRecommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Doing evaluation and cost observation, and takingaction while process don’t walk effectively or efficient.

b. Cost management process increased by kontinu’s alaand applies best internal performing.

c. Direct cost and indirect identified and reported by periodic ala and most automation on management, business process owner, and user.

d. All internal cost management expert is involved.e. Akuntabilitas and cost management accountability

services information be defined and is understood thoroughly at all level and backed up by formal training.

f. Cost reporting services to be linked by business aimand zoom deal services.

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6. DS8 Adjoins and give tips to user.Recommendation to make towards maturity zoom 3 De-fined Process :

a. distandarisasi’s procedure and is documented and bedone informal training.

b. Its made Frequently Asked Questions (FAQs) and userguidance.

c. Question and about problem is traced manually andkept company by individual.

d. Forte requirement in adjoins and give tips to identifieduser and documented comprehensive.

e. It develops formal training planning.f. Under one’s belt formal training for staff.

7. It does escalation about problem.Recommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Procedure for mengkomunikasikan, mengeskalasi andsolving about problem is formed and is communicated.

b. Staff help desk get direct interaction with managementstaff about problem.

c. diotomasikan’s peripheral and tech with gnostic basisabout problem and solution that centrally.

d. Person help desk coached and process is increasedthrough specific software purpose for work oneparticular.

e. Responsibility is worded and effectiveness is keptcompany.

f. The root cause of about problem identified and tren isreported, impacted on be done corrective aboutproblem ala periodic.

g. Increased process and applies best internalperforming.

8. DS9 Brings Off configurationRecommendation to make towards maturity zoom 3 De-fined Process :

a. Requirement for mengakurasikan and completes configuration information be understood and is applied.

b. Procedure and work performing is documented,distandarisasi and is communicated.

c. Configuration management peripheral that similardiimplementasikan at exhaustive platform .

d. It does automation to help deep traces changing equipment and software .

e. Configuration data utilized by interrelates process.f. Forte requirement in bring off identified configuration

and documented comprehensive.g. It develops planning and be done formal training.h. Ownership and configuration management Responsi

bility is established and restrained by responsible party.

i. Severally intent and measurement in configurationmanagement is established.

j. IT balanced scorecard applied in base performancemeasurement.

9. Its did supervisory deep brings off configuration.Recommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Procedure and configuration management default iscommunicated and is merged deep training, happening deviation will be kept company, traced and isreported.

b. Configuration management system enables to be doneit conducts distribution and release management onethat good.

c. Analisis is exemption and physical verification isapplied consistently and the root cause it is identified.

d. Peripheral most automation is utilized as technologyof emphasis to apply default and increases stability.

e. Forte requirement routinely at update to allconfiguration management process to get membershipand certification.

f. Training formaling to staff concerning data management is done according to plan and sharing was doneby sharing science.

g. Done by evaluation to trainings strategicaleffectiveness.

h. Configuration management responsibility defined byclear ala, established and is communicated deeporganisational.

i. Intent attainment indicator and performance havedisepakati user and monitored by process already being defined and concerned by business aim and TI’sstrategy plan.

j. Applied IT Balanced Scorecard in assessconfiguration management performance. Fixed up onan ongoing basis on configuration managementprocess is done.

10. DS10 Brings Off about problem and incidentRecommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Management process about problem comprehendedat all organisational deep level.

b. Method and procedure was documented, communicated, and is measured to reach effectiveness.

c. Management about problem and incident integratedby all bound up process, as changed as, availibility ofand configuration management, and adjoins customerin brings off data, facility and operate for. Peripheralpurpose most now was beginning exploitedappropriate strategical peripheral purposestandardization.

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d. Science and forte is perfected, looked after and is developed to superordinate level because information service function was viewed as asset and contributor ismain for attainment to the effect TI.

e. Responsibility and ownership gets clear character andacknowledged.

f. Ability responds to incident be tested by ala periodic.g. Largely about problem and incident is identified, re

corded is reported and is analysed for kontinu’s alastep-up and is reported to side stakeholder .

11. DS11 Brings Off dataRecommendation to make towards maturity zoom 3 De-fined Process:

a. Done by understanding socialization will datamanagement requirement so the need that wasunderstood and is accepted at firm as a whole.

b. Its issued kind of form letter of level management onfor gets to do effective steps in processes datamanagement.

c. Severally procedures to be defined and is documentedas basis in does severally base activity in datamanagement as process backup / restoration andequipment deletion / media.

d. Its arranged strategical purpose tools default to doautomation in data management system.

e. Its utilized many tools for need backup / restorationand equipment deletion / media.

f. Forte requirement in bring off identified data anddocumented comprehensive.

g. It does planning and formal training performing.h. Ownership and data management accountability is

established and about problem integrity and datasecurity restrained by party that accounts for.

i. Its established many aims and measurements in boundup data management with aim carries on business.

12. Observation and process measurement is done and ITbalanced scorecard applied in main performance mea-surement.Recommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Done by requirement socialization for whole ala datamanagement and needful action at organization.

b. Arranged periodic ala corporate internal forum for getsto look for solution with upstairs about problem whicharises deep data management.

c. Comprehensive procedures on process datamanagement, one that points on default, one thatapplies internal best practice , formalized anddisosialisasikan widely and is done sharingknowledge .

d. Purpose tools the newest one appropriate purposestandardization plan tools and dirintegrasikan withtools one that another. Tools that was utilized formengotomasikan to process main in brings off data.

e. Requirement skill routinely at update to all data management process to get membership and certification.

f. Training formaling to staff concerning datamanagement is done according to plan and sharingscience is done.

g. Done by evaluation to trainings strategicaleffectiveness.

h. Responsibility and ownership on data managementdefined by clear ala, established and is communicateddeep organisational. There is culture to giveappreciation as effort motivate this role.

i. Intent attainment indicator and disepakati’s performanceby user and monitored by process already beingdefined and concerned by business aim and TI’sstrategy plan.

j. Applied IT Balanced Scorecard in assess datamanagement performance and done by repair onan ongoing basis on data management process.

13. DS12 Brings Off facilityRecommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Requirement to nurse proceedings environment is conducted was understood with every consideration, thatthing most mirror on organization chart and budgetallocation.

b. sumberdaya’s recovering energy proceedings ismerged into organisational jeopardy managementprocess.

c. Formed planning for entirely organisational, availableintegrated examination and set and studied things ismerged into strategical revision.

d. Mechanism conducts default be attributed to draw theline access goes to facility and handle factor of safetyand environmentally.

e. Physical security requirement and environmental wasdocumented, access is kept company and restrainedby tights ala.

f. Integrated information is utilized to optimize insuranceand cost range that bound up.

g. Peripheral purpose most now was beginning exploitedappropriate strategical peripheral purposestandardization.

h. Severally peripheral was integrated with anotherperipheral and is utilized for mengotomasikan toprocess main in brings off facility.

i. Forte requirement routinely at update to all facilitymanagement process to get membership andcertification.

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j. Training formaling to staff concerning facilitymanagement was done according to plan and sharingscience was done.

k. Done by evaluation to trainings strategicaleffectiveness.

l. Responsibility and ownership was formed andicommunicated.

m. Facility staff was utterly been coached deep situatedemergency, as it were security and health performing.

n. Management keeps company effectiveness conductand compliance with standard one applies.

14. DS13 Brings Off to had outRecommendation to make towards maturity zoom 2 Re-peatable :

a. It imbeds organisational full care to main role that iscarried on hads out TI in provide TI’s supportfunction.

b. Requirement to do coordination among user andsystem operation is communicated.

c. Its did operate for support, operation default and TI’soperator training.

d. Budget for peripheral is allocated bases perkasus’s case.

15. Ownership and responsibility on management hadsout to be applied.Recommendation to make towards maturity zoom 3 De-fined Process :

a. Done by management requirement socializationcomputer operation in organisational.

b. Done by sumberdaya’s allocation and on the jobtraining .

c. Repetitive function is defined, documented and iscommunicated formally for person to had out andcustomer.

d. It does conduct afters tights one place to new talk shopon operation and formal policy to be utilized to reduceinstance amount that don’t scheduled.

e. Scheduling purpose most automation and anotherperipheral to be expanded and distandarisasi to drawthe line operator intervention.

f. Forte requirement in bring off identified data anddocumented comprehensive.

g. It develops planning and formal training performing.h. IT’s support activity another identified and bound

up duty assignment with that responsibility is defined.

16. Instance and task result already been solved isrecorded, but reporting goes to to side management in abind or uncommitted.Recommendation to make towards maturity zoom 4 Man-aged and Measurable :

a. Requirement for Management to had out whole ala wasunderstood and needful action was accepted widely atorganization.

b. Deviation of aught norm is solved and is correctedpresto.

c. Made by service deal and formal care with vendor.d. Operate for supported pass through sumberdaya’s

budget to capital expenditure and sumberdaya is man.e. sumberdaya’s purpose hads out to be optimized and

work or task working out already been established.f. Available effort to increase automation zoom processes

as tool to ensure kontinu’s ala step-up.g. Training is carried on and is formalized, as part of

career Pembangunan Jaya.h. Support responsibility and computer operation is

defined clearly and its ownership is established.i. Schedule and task is documented and is communicated

to TI’s function and business client.j. Done by fitting with about problem and supported

accessibility management process by analisis cause offailing and error.

k. Measurement and activity observation daily did byservice zoom and performance deal alreadydistandarisasi.

5. SHELL

Base examination already being done to hypothesises, haveresulted severally conclusion as follows:

1. IT ‘s processes on domain DS that basically beenneeded for gets to be applied. It pointed out by tallestimation result on option 4 (essential) and 5(momentously) on each IT ‘s process.

2. Entirely doman’s deep process Delivery and Supportneed for is done in TI’s management DignityPembangunan Jaya School. Largely IT’s processesbetter handled by TI’s job Unit Dignity PembangunanJaya Schools.

3. Largely increases current process maturity haven’treached expected target. To get up to target which isexpected therefore needed by penyetaraan’s steps thatdoing to pass through recommendation application oneach process which have maturity zoom gap.

4. Base gap whatever available, therefore prescribed remedial target that covers to process DS3, DS3, DS5,DS6, DS8, DS9, DS10, DS11, DS12, and DS13.

5. Process who will be inserted deep model managementIT will choose to base process that has to increasesmallest maturity and ekspektasi is largest management.DS13’S process (Doing indentifikasi and costallocation) constituting process that has to increasesmallest maturity and ekspektasi is largest management.

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Allocable tips of yielding observational it for example:

1. Managements model design IT Dignity PembangunanJaya School needs to be perfected through feedbackor acquired entry while do implementation.

2. On every attainment phase passed on by action thatneed to be able to been done aught gap mantle. Oneach attribute maturity given by needful action.

3. Proposal sets to bring off TI this ought to gets to belooked backward by periodic ala to be donePembangunan Jaya according to technology progress.

LITERATURE

1. [IGI 2000] he COBIT Steering Committee and the IT Gov-ernance Institute, COBIT (3rd Edition) Implemen-tation Tools Set, IT Governance is Institute, 2000 .

2. [IGI 2003] he IT Governance Institute, Board Briefing onIT Governance, 2nd Edition, IT Governance Insti-tute, 2003.

3. [IGI 2005] he IT Governance Institute, COBIT 4.0: Con-trol Objectives, Management Guidelines, Matu-rity Models, IT Governance Institute, 2005.

4. [GULDENTOPS2003] Guldentops, E. (2003), Maturity Measurement First

the Purpose, Then the Method, Information Sys-tems Control Journal Volume 4, Information Sys-tems Audits and Control Association, 2003.

5. [INDRAJIT 2000] ndrajit, Echo r.., Information Systemmanagement and Information Technology, Gramedia,Jakarta, 2000.

6. [JERRY 2001] Jerry Fitzgerald, of system analysis’sfundamental, 2001.

7. [JOGIYANTO 2003] ogiyanto, Analisis and Design, Andi,Yogyakarta, 2003.

8. [KHALIL 2000] Khalil, Tarek M., Management of Tech-nology: TheKey to Competitiveness and WealthCreation . International ed., McGraw Hill, 2000.

9. [PEDERIVA 2003] ederiva, A, The COBIT Maturity isin’s Model a. Vendor Evaluation Case, InformationSystems Control Journal is Volume 3, InformationSystems Audits and Control Association, 2003

10. [ROBERT 2003] obert a., Accounting Information Sys-tems (Prentice’s new jersey Hall, 2003)

11. [REINGOLD 2005] eingold, S., Refining IT ProcessesUsing COBIT, Information Systems Control Jour-nal is Volume 3, 2005, Information Systems Auditsand Control Association, 2005

12. [VAN 2004] an Grembergen, W., De Haes, S.,Guldentops, E., Structures, processes and RelationalMechanism for IT Governance, in for InformationTechnology Governance’s Strategics, Grembergen’svan, W, Idea’s editor Inc’s Group, 2004

13. [WEBER 1998] Weber, Ron., Information Systems Con-trol and Audits. Prentice is Hall, 1998.

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Paper

STMIK Raharja - [email protected]

AbstractionUse of remote control system has been increasing in line with the globalization era in which human movement and thebroad and quick movement. At this time the public has known that for the controlling of an electronic home appliancesfrom remote can be done using the remote control. The problem of remote control by the limited distance between thesignal emanated by the remote and the signal received by the electronic home appliances, when the distance betweenhome electronic appliances which are controlled by a remote/controller through limit tolerance. To solve the problemthen the system must be designed using network technology that can be accessed anywhere and at anytime during theavailability of network. Internet network used by TCP/IP-based module starter kit network NM7010A-LF as a bridgebetween the AVR microcontroller system with a computer network for controlling the electronic equipment, AVRmicrocontroller system works as a web server. The result is a prototype of electronic home appliances that can controlhome electronic appliances from a remote that is not barred by the distance, place and time so that ultimately improve theeffectiveness, efficiency and comfort in control.

Keywords : NM7010A, TCP/IP, AVR, ElectronicHome Appliances

Saturday, August 8, 200916:50 - 17:10 Room L-212

ELECTRONIC CONTROL OF HOME APPLIANCES WITH IP BASEDMODULE USING WIZNET NM7010A

Asep Saefullah, Augury El Rayeb

237

I. INTRODUCTIONIn general, people with tools such as remote controllerremote that can control an electronic equipment, such astelevision, audio, video, cars, and so forth. Remote controlusing constrained by the limited ability of the remote in asignal that will be shed by the recipient. So that the use ofremote control is limited by distance, when distance be-tween equipment is controlled with a controller that passesthe tolerance limit, then the equipment can not functionaccording to the desired.

Internet is an extensive network of global interconnectand use TCP/IP protocol as the exchange of packets (packetswitching communication protocol) that can be accessedby anyone, anywhere, can be used for various purposes.Internet also has a large influence on the science, andviews the world.

To solve the problems of limited distance from the remotecontrol, the internet is a technology solution to be imple-

mented. Applications will utilize TCP/IP Starter Kit basedon the network module NM7010A-LF as a bridge betweenthe AVR Microcontroller System with computer network.The AVR Microcontroller System will function as a webserver in the making of tool-distance remote controller.TCP/IP Starter Kit which is a means of developing TCP/IPbased on NM7010A module network that functions as abridge between microcontroller with internet or intranetnetwork without requiring computers assistance. TCP/IPis suitable for embedded applications that require commu-nication with the internet or intranet.

Hypothesis is by using a remote control system throughthe internet media, will make a control system which is nolonger limited by distance and time. Process control canbe done anywhere and anytime. Control system using theinterface protocol TCP/IP starter kit and combined withthe microcontroller technology can produce a performanceof the remote control more practical and efficient.

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A. PROBLEMSThe limited distance control through the remote control iscaused by the limited power scattered from a remote con-trol, the control can not be done from a distance as farmore extreme is the place or city. To control remotely theelectronic equipment, the distance between the means ofcontrol and that control must be in the range of the remotecontrol coverage distance. Internet is an appropriate solu-tion, because it is not by the distance, where there are theinternet connection controlling can be made.

To make communication between equipment that will becontrolled with the control, needed a tool that can controlthe IP-based communications, and that tool is WiznetNM7010A module. Next problem is how the modules(Wiznet NM7010A) can communicate with microcontrollerwhich is a tool to control actuator (electronic home appli-ances).

II. DISCUSSION

Internet technology can be used to overcome the distancein a remote controlling system, for example in the case ofcontrolling the equipment in an electricity industry. With-out being restricted by space and time, controlling theprocess can be done from anywhere from a place that hasinternet access. By controlling the system remotelythrough internet, system controlling is no longer only forthe local scope, but global.

Process control can be done anywhere, anytime withoutthe officer / operator to come. System control can be donevia the Internet to test and supervision so that more prac-tical and efficient. The diagram’s for remote controllingsystem via the internet protocol (IP) can be seen below.

Each sub-system in this design has the function and tasksthat related with one another, there are 6 sub-blocks of thesystem will be described in relation with the system thatwill be developed as follows :ComputerPower SupplyRS 485 ConverterMicrocontroller AVRDriverElectronic home appliances

ComputerElectronic home appliancesAVR MicrocontrollerDriverRS 485 ConverterPower Supply

Figure 1. Diagram of embedded ip system

Computers are used to make the program to the AVRmicrocontroller, while a series of RS 485 converter as amediator of the computer to the AVR mikrokontroler tocontrol home electronic appliances. the software require-ment for making the program is as follows :1. Windows 98, ME, XP dan 20002. Code Vision AVR Downloader3. BasCom AVR

Each component requires DC power, both of RS 485 con-verter, microcontroller and driver. Therefore, the systemrequire power supply. The schema for power supply canbe seen below

Figure 2. Schematic for power supply

RS 485 Converter module is used as an interface betweenmicrocontroller with internet network through ethernet. RS485 Converter module is a network-based module usingNM7010A with the following specifications :

a. NM7010A Based who can handle the internal communication protocol (TCP, IP, UDP, ICMP, ARP) andethernet (DLC, MAC).

b. Using the I2C interface technology for communicationwith microcontroller.

c. Mini I2C address can be selected from the 128 addressoptions that are available (0, 2, 4...252, 254)

d. Equipped with LED status indicators as the network(collision / link, 10/100 act, full / half duplex)

e. Requires 5 Volts DC power supply and has a voltageregulator 3,3 Volts DC with 300 mA currents.

f. Compatible with DT-AVR Low Cost Series system controllers and also support other controller.

The outputs of microcontroller control the drivers that willcontrol the electronic home appliances, electronic home

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appliances is the end of the load control. Before enteringinto the actuator, output of mikrokontroler need to bestrengthened through the driver

A. AT mega 8535 AVR MicrocontrollerAVR Microcontroller is one type of Microcontroller’s ar-chitecture that a mainstay atmel. This architecture has beendesigned to have various advantages among other exist-ing atmel’s microcontroller. One of the advantages is theability of In System Programming, so AVR microcontrollerchip can be programmed directly in the system into a se-ries of applications. In addition, AVR has Harvard architec-ture that uses the concept of memory and a separate busfor data and program, and already operates a single pipelinglevel, so that instruction execution can take place veryquickly and efficiently.

Figure 3. AT 90S8535 AVR Microcontroller pin out con-figuration

(Source: Prasimax Mikron Technology Development Cen-ter :2007 )

A. Hardware DesignHardware design of Wiznet NM7010A network module asseen in figure 4 and figure 5. Figure 5 is a schematic diagram’sof NM7010A while the block diagram’s shown in figure 4.This design will use NM7010A network module as a bridgebetween DT-AVR Low Cost Micro System with computernetworks to create a simple web server. Program was de-veloped using the compiler BASCOM-AVR © version1.11.8.1. In this BASCOM-AVR © compiler there are com-mands that support the interface with the module NM7010A.

.

Figure 4. Diagram’s of TCP/IP Starter Kit NM7010A

Figure 5. Schematic’s of TCP/IP Starter Kit NM7010A

A. I2C (Inter-Integrated Circuit) CommunicationTo connect the TCP / IP Starter Kit NM7010A with DT-AVR Low Cost Micro System which was used I2C proto-col with two cable SDA (Serial Data) and SCL (Serial Clock)for sending data in serial. SCL is a path that is used tosynchronize the data transfer on the I2C lines, while theSDA is for a data path.

Some devices can be connected to the I2C in the samepath where the SCL and SDA be connected to all devices,but there is only one device that controls the SCL is themaster device. The path from the SCL and SDA is con-nected to the pull-up resistor with the resistance valuebetween 1K to 47K (1K, 1.8K, 4.7K, 10K, 47K).

With the pull-up, SCL and SDA line to be open drain, whichmeans the device is only necessary to give the signal 0(LOW) to create a path to be LOW, and leave it blank (orno signal) the pull-up resistor will make the path to beHIGH. In I2C devices that have a role only one device to

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be master (although some of the device possible, in thesame I2C lines, a master) and one or more slave devices. InI2C path, only the master device can control the SCL line,which means the data transfer must initialized first by themaster device through a series of clock pulse (not slaves,but there is one case which is called clock streching). Slavedevice task only responds to what the master device re-quired. Slave can send and receive data to master, afterserver initiate.

Master device must do some initialization before transferingor send/receive data with it’s slave devices. Initializationbegins with the START signal (high to low transition onthe SDA line, and high conditions on the SCL line, symbolS on the figure 7), and then data transfered and after that,the STOP signal (low to high transition on the SDA line,and high conditions on the SCL line, symbol P on thefigure 7) to indicate end of data transfer.

A large number of bytes may be sent in a data transfer,there are no rule for that. If you want to transfer the datathat made of 2 bytes, the first delivery is 1 byte and 1 bytemore after that.

Figure 6. Start / Stop Signal

(Source: UM10204 I2C-bus specification and usermanual)

Each byte in the transfer must be followed by an Acknowl-edge bit (ACK) from the receiver, indicates the data wassuccessfully received. Bytes sent from the sender beginwith MSB bit. When bit is sent, the pulse clock (SCL) is setto HIGH to LOW ago. Bits sent in the SDA line must bestable during the period clock (SCL) HIGH. LOW or HIGHcondition of the data path (SDA) can only be changedwhen the condition signal SCL is LOW.Figure

7. Transfer bit on I2C path

(Source: UM10204 I2C-bus specification and usermanual)

Each clock pulse that generate (on SCL path) are for everybits data (on SDA path) that tranfer. So to allow 8-bit willhave 9 pulse to be generated in clock pulse (1 bit for ACK).The chronology for the receiver device before provide sig-nal ACK is as follows: when the sender is finished to sendthe last bit (8th bit), sender release the SDA line to the pull-up (remember the description open drain) so that a HIGH.When such conditions occur, recipients must provide con-ditions LOW to SDA when the 9th clock pulse is located inthe HIGH condition.

If the SDA remains in HIGH conditions when in the 9th

clock pulse reach, then this signal is defined as a Not Ac-knowledge (NACK). Master device can generate a STOPsignal to finish the transfer, or repeat the START signal tostart a new data transfer.

Figure 8. Data (byte) transfer on I2C path

(Source: UM10204 I2C-bus specification and usermanual)

To implement I2C protocol, will take samples from the rou-tine of Peter Fleury’s and CodeVision AVR I2C routine (us-ing the C language). The first thing that happened in thecommunication is server send START signal. This will in-form slave devices are connected in I2C path that will havedata transfer to be done by the master device and the slavedevices must be ready to monitor who’s address will becalled. Then the master device will send data such as ad-dress of slave device who want to access. Slave devicewith the appropriate address given by master device willforward the transaction data, the other slave device canheed the transaction and wait until the next signal. Afterget the slave device that match to the address, it’s time forthe master device to inform the internal address or register’snumber to be written to or read from the slave device. Num-ber of locations or register’s number is dependent on theslave device is accessed. After sending data forming theslave device address and then the address of register in-ternal slave who want to access, now is time to send themaster data bytes. Master device can continue to senddata bytes to slave device and byte by byte will be storedin the register after that the slave device will automaticallyincrease the internal register address after each byte. Whenthe master device has finished writing all data to the slavedevice, the master device will send a STOP signal to termi-nate data transaction. For the implementation of the I2Ccode, used for examples I2C routines for AVR from PeterFleury’s routine and I2C routines provided in CodeVision

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AVR.

A. DESIGNIn the module NM7010A, Set DIP Switch J3 on the TCP/IPStarter Kit for the I2C address = CCH, set the switch 2, 3, 6,7 to OFF position and switch 4, 5, 8 to ON position. Afterthe module connected and share resources with the cor-rect, open NM7010A.BAS using the BASCOM-AVR andchange line 50 on the program to fit the computer networkthat will be used. For example:§ For the computer network that have gateway, andthe network setting value:Gateway = 192.168.1.2Subnet Mask = 255.255.255.0IP = 192.168.1.88 (nomor IP dari modulTCP/IP Starter Kit)

Then change line 50 on the program to become like this:Config Tcpip = Int0 , Mac = 12.128.12.34.56.78 , Ip =192.168.1.88 , Submask =255.255.255.0 , Gateway = 192.168.1.2 , Localport = 1000 ,Tx = $55 , Rx = $55 , Twi =&HCC , Clock = 300000

§ For the computer network that don’t have gate-way, and the network setting value:Subnet Mask : 255.255.255.0IP modul : 192.168.1.88 (nomor IP dari modul TCP/IP StarterKit)

Then change line 50 on the program to become like this:Config Tcpip = Int0 , Mac = 12.128.12.34.56.78 , Ip =192.168.1.88 , Submask =255.255.255.0 , Gateway = 0.0.0.0 ,Localport = 1000 , Tx = $55 , Rx = $55 , Twi = &HCC,Clock = 300000

After that program NM7010A.bas re-compile and thendownload the results of compilation in the DT-AVR LowCost Micro System using DT-HiQ AVR In System Program-mer (ISP) or other device that supports mikrokontrolerATmega8535. Then connect to the network computer sys-tem and run Microsoft Internet Explorer from the computerconnected to the network computer. Type in http:// <nomorIP> / index.htm (for example http://192.168.1.88/index.htm)on the Address bar, Microsoft ® Internet Explorer © thenshow your site’s pages from this embedded web server.[* File contains invalid data | In-line.JPG *]

Figure 9. NM7010A-LF module

B. Software DesignMicrocontroller is the electronics components that it’sperformance depend on the program that entered and hasbeen working on. Before Microcontroller used in the sys-tem electronics chain, first must be filled with program thatwas created by the programmer. Software program thatusually used to write the listing in assembly language pro-gram is BASCOM-AVR.

The flowchart program to the target block is as follows:

Figure 10. Flowchart Program to Target Block

A. Code Vision Atmega 8535CodeVisionAVR software is a C cross-compiler, where theprogram can be written using C-language. By using the C

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programming language-expected design time (developingtime) become shorter. After the program in C language-written and after a compilation results no error (error free)then the download the hex file to microcontroller can bedone. AVR microcontroller support the ISP (In-System pro-gramming) system download.

A. The process of programThe program’s process of NM7010A.BAS in general is asfollows:

1. Program will reset NM7010A module by hardware,activate the microcontroller’s interrupt and doinitialization to module NM7010A in I2Ccommunication mode.

2. Then make a declaration of program variables that willbe used, among other:

§ shtml as a string with 15 characters long to save thesuffix from the command that received.

§ Ihitcounter, Ihitcounter work as an integer to store thenumber of visitors to this webserver.

3. Program get the status from socket 0.

4. If the status of socket 0 = 06h (established), then:a) The program will check the Rx buffer from module

NM7010A, and if there is data received in the Rx bufferthen the program will read it.

b) If the data received is the command “GET” then theprogram will save a suffix that follow the command intoa variable Shtml.

c) The Program check if Rx buffer is empty, if not emptythen the program will return to step 4.a.

d) If the Rx buffer is empty then the program sends“HTTP/1.0 200 OK <CR><LF>” (OK sign) and alsosend “Content-Type: text/html <CR><LF>” (format forHtml body that will be sent).

e) If Shtml = “/index.htm” then program will send the bodyof index.htm and add the value of the variableIhitcounter with 1.

f) Program delete the contents of variables Shtml, andthen close the socket 0 and return to step 3.

5. When the status of socket 0 = 07h (wait connectionclose) then the program will close the socket 0 andreturn to step 3. 6.

If the status of socket 0 = 00h (connection closed) then

the program open the port 80h socket 0 and start listeningto the network from socket 0, then the program returns tostep 3.1.

Figure 11. Web page web server based on embedded IPon Ms. Internet Explorer

Web page of this application consists of a header, text,and a visitor counter, as shown in Figure 11. This applica-tion can be developed into more complex, for example, tosend data from sensor dan to control devices throughcomputer network.

A. Writing Assembly Program on MicrocontrollerMicrocontroller is one of electronics components typesthat it’s performance depends on the program in assemblylanguage that filled into mocrocontroller and has beenworking on. So that for microcontroller to work and sup-port these systems work as desired, it must first filled withthe correct assembly program, both in terms of assemblylanguage and how the program contents or filled.

Before the microcontroller used in the electronics systemchain, the microcontroller must be filled with program thatwas created by the programmer. The purpose of that ac-tion is to make this embedded IC work in accordance withthe desire. Software used to write program listing in as-sembly language is BASCOM-AVR, the reason for usingthis software has some advantages as compared to othersoftware.

After writing the program listing’s on the BASCOM-AVRtext editor is complete, then the text is stored in files withnames MOTOR DC.BAS (for example), this must be donebecause the software only works on file with the name *.BAS.

The next step is to compile the Basic language file into hexfile, that file will be MOTOR DC.BAS make MOTORDC.HEX file by pressing the F7 key on the keyboard or viathe menu. This *.Hex file will be inserted or downloaded

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into the IC Atmega8535 (microcontroller). The steps abovecan be seen in the figure 12 below.

Figure 12. Program Compilation

After those steps are done, then we will have some filesafter the compilation step, namely: MOTOR DC.BAS,MOTOR DC.HEX (for example, means that we have 2 files*.bas as source code and *.hex as compiled code) andseveral other supporting files. and until this stage in theprocess of writing and compiling the assembly program iscompleted.

A. Downloading Program into MicrocontrollerAt this step, the IC Atmega8535 initially filled with emptystart the program. While for the IC that already containsthe program, the program must be deleted first before au-tomatically filled in with the new program. To begin, firstopen the program BASCOM-AVR for mikrokontrolerAT8535, then select the device that will be used, namelyAT8535.

Figure 13. Choosing a Device (AT8535)

After selecting a device from compiler tab. Then the appli-cation ask for the hex file that will download into selecteddevice (microcontroller AT Mega 8535). Hex akan that en-tered into the IC mikrokontroler, in this case is MOTORDC.HEX (for example).

Hex File that opened into application will be recognized bythe software then downloaded into microcontroller. thenclick the chip menu and select the Auto program, as seenbelow.

Figure 14. Proces of filled in the program into microcontroller

Downloading process begins with the “erase Flash &EEPROM Memory”, which means the software performdeletion of microcontroller’s the internal memory beforeput the program into microcontroller’s the internal memory.In the process of deletion of this, when percentage hasreached 100% then means that the internal memory hasbeen erased completely and in a state of empty. If percent-age has not reached 100% but the software shows an errorsign, then the elimination process is fail. This is usuallycaused by an error in the hardware downloader.

After the removal finished, software automatically “VerifyFlash Memory”. Software start download the hex file to fillthe program into microcontroller. As with the deletion, theprocess is shown with percentage of progres. 100% indi-cates that program has been fully filled into microcontroller.The emergence of a sign error indicates the process failed,which is usually caused by errors in the hardwaredownloader. If the steps above done correctly, then themicrocontroller (AT 8535) is ready and can be used to runa system as desired.

A. Downloading Program into Microcontroller with Starterkit Compiler that commonly used with AVR microcontrolleris C Compiler. CodeVision have been provided on the edi-tor to create a working program in C language, after thecompilation process, we can put the program was createdinto memory of microcontroller using a program that hasbeen provided by CodeVision AVR.

ISP (In System Programming) device types that is sup-ported by CodeVision AVR many variations, among oth-ers: Kanda Systems STK200 + / 300, Atmel STK500/AVRISP,

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Dontronics DT006, and others. To make “de Kits AVR ISPProgrammer Cable CodeVision” can be integrated with theAVR, the following configuration be done:- Run The CodeVision AVR Software.- Choose Setting’s menu ’! Programmer.- Choose type of ISP programmer ’! Kanda SistemSTK200+/300.- Then click OK Button

Figure 15. Choosing ISP Programmer in CodeVision AVR

Once CodeVision AVR has been configured, do test to “deKits AVR ISP Programmer Cable” by connect it with thetarget board, and to the PC through LPT port, as shown infollowing picture (figure 16):

Figure 16. Atmega 8535 Programmer Cable Connection

The black housing to connect the ISP header on the targetboard is adjusted to the layout of the pin. Pin layout on the

black housing “de Kits AVR ISP Programmer Cable” are asshown in figure 17. Because the black housing has a formof symmetrical, so the only sign as a guideline is a sign ofthe triangle on one side black housing where the pin closeto the marker is the VCC pin 2.

Figure 17. AT 89S51 pin configuration

To perform a test to “de Kits AVR ISP Programmer Cable”,start a new project as follows:

§ Place the AVR ISP Programmer Cable on the targetboard that already connect to the targetmicrocontroller.

§ Select the Tools menu ’! Chip Programmer, or pressShift + F4.

§ In the window Chip programmer menu select Read ’!Chip Signature.

§ When the AVR ISP programmer cable works well andID microcontroller not damaged, then the target typemikrokontroler will look like the picture below(figure 18).

Figure 18. de KITS AVR ISP Programmer Cable test withthe Read Chip Signature

(source link: www.innovativeelectronics.com)

This proses can only be done when there is a project open.Press Shift+F9, download to target board by click on pro-gram button. After that the microcontroller ready to use.

III. SUMMARY

Based on test and design to the control module can be

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summerized that:a) DC motor as the electronic home appliances controlled

through the internet media with NM7010A networkmodule. Contolling the DC motor remotely can be doneBy pressing F5 button on computer’s keyboard andthe computer connected to internet. We can controlthe direction of it’s (DC Motor) rotation.

b) By using TCP/IP Starter Kit based on NM7010A network module as a bridge between DT-AVR Low CostMicro with Network or Internet, controlling the electronic home appliances can be done remotely withoutany barrier in distance and time.

LITERATURES

1. Asep Saefullah, Bramantyo Yudi W, (2008), PerancanganSistem Timer Lampu Lalu Lintas DenganMikrokontroler AVR, CCIT Journal, Vol.2 No.1,STMIK Raharja

2. Paulus Andi Nalwan, (2003), Panduan Praktis TehnikAntar Muka dan Pemrogaman MikrokontrolerAT89C51, Gramedia, Jakarta

3. Untung Rahardja, Asep Saefullah, (2009), SimulasiKecepatan Mobil Secara Otomatis, CCIT Journal,Vol.2 No.2, STMIK Raharja

4. Widodo Budiharto, (2005), Perancanan Sistem DanAplikasi Mikrokontroller, PT. Elex MediaKomputindo, Jakarta

5. Widodo Budiharto, Sigit Firmansyah, (2005), ElektronikaDigital Dan Mikroprosessor, Penerbit Andi,Yogyakarta

6. Wiznet7010A, (2009), http://www.wiznet.co.kr/en/pro02.php?&ss[2]=2&page=1&num=98, accessedon June 3th, 2009

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Computer Science Department, Faculty of Mathematic & Natural Sciences, Pakuan University1) [email protected], 2) [email protected], 3) [email protected]

AbstractDiagnostic system of Dermatitic based on Fuzzy logic constructed with seven indication variables. These variableshave different intervals and used for determining status of domains in membership function of variables. The domainsclassification identified as : very light, light, medium, heavy, chronic. The classification obtained from intuition andconfirmed to the expert. Membership function which built based on fuzzy rule base; consist of 193 rule and part ofFuzzyfication input. System implemented with using MATLAB 7.0. Output was Dermatitic diagnostic-divided into :Static Dermatitic (10-20%), Seboreic (21-35%), Perioral Dermatitic (36-45%), Numular Dermatitic (46-55%),Herphetymorfic Dermatitic (66-80%), Athopic Dermatitic (81-90%), Generalyseate Expoliate Dermatitic (91-97%).

Keywords: dermatitic, fuzzy logic, domain, membership function, fuzzy rule base.

Saturday, August 8, 200915:10 - 15:30 Room L-210

DIAGNOSTIC SYSTEM OF DERMATITIC BASED ON FUZZY LOGICUSING MATLAB 7.0

1. INTRODUCTIONThe application of computer for disease diagnostic ishelpful with fast and accurate result. In disease diagnostic,paramedics often seem to be doubt full since some ofdiseases have indication that almost the same. Thereforemodel of Fuzzy logic needed to solve the problem.Fuzzy logic (obscure logic) is a logic which faced withhalf of true concept between 0 and 1. Development oftheories shows that fuzzy logic can be used to model anysystems including Dermatitic DiagnosticCrisp input converted into fuzzy data with fuzzymembership function by fuzzyfication, on contraryconvertion output of defuzzyfication into wanted data ie.Result of Dermatitic diagnostic.Selected language programme is MATLAB 7.0 fullfilledfuzzy logic toolbox which form fuzzy inference system(FIS). Facilitating interaction between users and system,MATLAB 7.0 provides Graphic User Interface (GUI) usingscript*.m.files.

2. PROBLEM ANALYZINGDiagnostic of Dermatitic based on physical indicationexamination and medical patient complaint, then defined

as fuzzy variable. Indication variables including itchiness,redness,swelling, skin scab, skin scale, skin blist and skinrash. For determining domain of fuzzy association, directinterviews to the expert were used.Tables 1. The Fuzzy variables

Eneng Tita Tosida, Sri Setyaningsih, Agus Sunarya

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Figure 3. Itchiness membership function

Membership function of whole itchiness inputvariables defined as :

From above rule can be identified ichtiness variableswhich categorized as : very light, light, medium, heavy,cronic. The process which held on whole of membershipfunction for input variables showed in Figure 4-9respectively.

Figures 4. Redness membership function

3. FORMING FUZZY ASSOCIATION AND SYSTEM INPUT-OUTPUT VARIABLE MEMBERSHIP FUNCTION

Function model for starting and ending fuzzy regionvariables was shoulder-form curve, while for crossing wastriangle curve (Kusumadewi, 2004). Domain of any fuzzyassociations which had been formed can be seen in Tables

Tables 2. Fuzzy Associations

Membership function for itchiness input variablescan be seen in Figures 3.

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Figures 5. Swelling membership function

Figures 6. Skin scab membership function

Figures 7. Skin scale membership function

Figures 8. Skin blist membership function

Figures 9. Skin rash membership function

Membership function of desease output variablescan be seen in Figures 10.

Figures 10. Desease membership function

Membership function of desease output variables can bedefined as :

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Based on modeling process and verification resultof the expert/physician for Dermatitic diagnostic system,there were 193 fuzzy rulez* =+” µc (z) · z dz

+” µc (z) dz

Defuzzification process aims to change fuzzy value tocrisp. At

Figures 11. Flowchart of system

Result of system implementation step with Matlab 7.0shown in Figures 12-14, respectively :Mamdani rule composition, there are some defuzzymethod, one of them is Centroid Method (CompositeMoment). From this method, the crisp of output variablescounted with finding variables value z* (center of gravity)of its membership function.

4. DESIGN AND SYSTEM IMPLEMENTATIONFlowchart of Dermatitic Diagnostic system based onFuzzy logic is shown in Figures 11.

Figures 12. Membershipship Function

Figures 13. Rule Editor

Figures 14. Fuzzyfication process

Running out of main form programme can be seenin Figures 15 :

Figures 15. Main form and Diagnostic process

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At diagnostic form, there were seven input (value = 65),these values were user input values and these ones hadcertain fuzzy values. While at result of diagnostic, therewere 75 which obtained from FIS and herphetymorficdermatitic was a kind desease of diagnostic result.

5. VALIDATIONThe following case was comparison between expert

with programme output data, if itchiness = 17%, redness= 24%, swelling = 15%, skin scab = 13%, skin scale = 25%,skin blist = 19% and skin rash = 22%, so finding of anyinput membership degrees agree with previousmembership function. Value of fuzzy membership foritchiness variables at any associations :Very light fuzzy association [17] = 0.65Found fromf[17] = (30-17)/20 = 0.65Light fuzzy association [17] = 0.35, found from :f[17] = (17-10)/20 = 0.35Medium fuzzy association [17] = 0Heavy fuzzy association [17] = 0Cronic fuzzy association [17] = 0

It was held for any input variables.

8. Fuzzy InterferenceFuzzy interference process used min-max rule, then

withdrawn the highest value from the result of first countusing OR command.

Based on the result, it could be determined that theselected rule was rule no. 65, therefore the diagnostic resultwas 65% medical patients suffer Static Dermatitic. Theouput system shown in Figures 16.

Figures 16. Output of diagnostic

output produced certain Dermatitic diagnostic.One of problem in this research was determining

fuzzy membership function in

system building since there was not standard formyet released out by the expert. Therefore the resultobtained at real data examination unappropriate with partof ouput data programme.

7. REFERENCES[1] Abdurohman, A. Bab 2, http://

www.geocities.com/arsiparsip/tatf/ta-bab2.htm,2001.

[2] Goebel, G, An Introduction To Fuzzy ControlSystems, Public Domain. http://www.faqs.org/faqs/, 2003.

[3] Gunaidi, AA., The Shortcut MATLABProgramming, Informatika, Bandung, 2006.

[4] Kristanto, A., Kecerdasan Buatan, Graha Ilmu,Yogyakarta, 2004.

[5] Kusumadewi, S., Analisis & Desain Sistem FuzzyMenggunakan Toolbox Matlab, Graha Ilmu,Yogyakarta, 2002.

[6] Kusumadewi, S & Purnomo, H., Aplikasi LogikaFuzzy untuk pendukung keputusan, Graha Ilmu,Yogyakarta, 2004.

[7] Marimin, Teori dan Aplikasi sistem pakar dalamteknologi manajerial, IPB Press, Bogor, 2005.

[8] Panjaitan, L.W., Dasar-dasar Komputasi Cerdas,C.V ANDI OFFSET, Yogyakarta, 2007.

[9] Piattini, Galindo & Urrutia, Fuzzy DatabasesModeling, Design and Implementation, Idea GroupPublishing, London, 2007.

[10] Sugiharto, A. Pemrograman GUI (Graphic UserInterface) dengan MATLAB, C.V ANDI OFFSET,Yogyakarta, 2006.

[11] Sumathi, Sivanandam & Deepa, Introduction toFuzzy Logic using MATLAB with 304 figure and37 tables, Springer, Berlin, 2007.

[12] http://www.fuzzytech.com 25-Des-2007[13] h t t p : / /

www.medicastore.com\med\kategori_pyk18e5.html06 Juni 2007

[14] h t t p : / / w w w. m e d i c a s t o r e . c o m / m e d /subkategori_pyk04f0.html?idktg=14&UID=20071118183038202.182.51.23001-Januari-2008

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14:20 - 14:40 Room AULA

Design of Precision Targeting For Unmanned Underwater Vehicle(UUV) Using Simple PID-Controler1

aDepartement of Mechanical and Industrial Engineering, Gadjah Mada University,Jl. Grafika 2 Yogyakarta. 52281. Email: [email protected], Phone: 08122698931

bFaculty of Mathematics and Natural Sciences, Gadjah Mada University,cDepartement of Geodetical Engineering, Gadjah Mada University,

ABSTRACTA precision targeting for torpedo using simple PID controller has been performed to get a solution model. The system hasbeen assumed to have two-dimansional character, such that the mechanical control mechanism would be performedsolely by rudder. A GPS/IMU system was employed in the model to provide the exact location and current trajectorydirection and will be used to compared between the instataneous correct direction and instataneous current direction.This difference would drive PID control system to give correct angle deflection of the rudder. Some parameters of the PIDcontroller has to be well-tunned employing several schemes including the Routh-Hurwitz stability criterion.

Keywords: Torpedo, UUV, PID Controller, Precision Targeting

Nomenclature:è : drift angleG : centre of grafityF : rudder forceT : propeller thrustRx : resistance (drag)Ry : additional resistance due to turning

motionJ : Moment inertia polar from M’ to Gç : “è = è0 – è1.á : rudder deflection angleñ : instantaneous radial curvature

IntroductionAbout two-third of the earth are covered by oceans. About37% of the world population lives within 100 km of theocean (Cohen, et al., 1007). The ocean is generally over-looked as we focus our attention on land and atmosphericissues, we have not been able to explore the full depth ofthe ocean and its abundance living and non-living re-sources. However a number of complex issues due to the

unstructured, hazardous undersea environment make itdiffcult to travel in the ocean. The demand for advancedunderwater robot technologies is growing and eventuallylead to fully autonomous, spacialized, reliable underwaterrobotic vehicles. A self-contained, intelligent, decision-making AUV is the goal of current research in underwatervehicles.Hwang, et al.(2005) have proposed an intelli-gent schemeto integrate inertial navigation system / global positioningsystem (GPS) with a constructive neural network (CNN) toovercome the limitation of current schemes, namely Kalmanfiltering. The results has been analyzed in terms of posi-tioning accuracy and learning time. The preliminary re-sults indicates that the positioning accuracy were improvedby more than 55%, when the multi-layer-feed-forward neu-ral network and CNN based scheme were implemented.Huang and Chiang (2008) have proposed low cost attitudedetermination GPS/INS integrated navigation system. Itconsists of ADGPS receiver, NCU, low-cost MEMS IMU.The flight test results shows that the proposed ADGPS/

Sutrisno, Tri Kuntoro Priyambodo, Aris Sunantyo, Heru SBR

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INS integrated navigation system give reasonable naviga-tion performance even when anomalous GPS data was pro-vided.Koh et al. (2006) have discussed a design of control mod-ule for UUV. Using modelling, simulation and experiment,the vehicles model and its parameters have been identi-fied. The mode cotroller gain values was designed usingnon-linear optimizing approach. Swimming pool tests haveshown that the control module was able to provide rea-sonable depth and heading action keeping.Yuh (2000) has surveyed some key areas in current state-of-the-art underwater robotic technology. Great efforts hasbeen made in developing AUVs to overcome challengingscientific and engineering problems caused by the unstruc-tured and hazardous ocean environment. In 1990, 30 newAUV have been built worldwide. With the development ofnew materials, advanced computing and sensory technolo-gies, as well as theoretical advancement, R&D activities inthe AUV community increased. However, this is just thebeginning for more advanced, yet practical and reliableAUVs.

Rudder LiftRudder Drag

Abdel-Hamid et al. (2006) have employed offline pre-de-fined fuzzy model to improve the performance of integratedinertial measurement units (IMU) utilizing micro-electro-mechanical-sensors (MEMS). The fuzzy model has beenused to predict the position and velocity error, which werean input to a Kalman filter during GPS signal outage. Thetest results indicates that the proposed fuzzy model canage.

PROBLEM DEFINITION

The focal point of this paper is the development of Indo-nesia defense technology. The Indonesia defense tech-nology should not depend strongly on foreign technol-ogy, we had to develop our own technology. The compo-nents of our military technology should be able to be foundin the open market without any fear becoming the victim ofembargo.

Fig. 2. Several possible trajectory for UUV which itscurrent direction è1 toward the reference direction è0. Theresponse could be a) the wrong trajectory due to not enoughcorrection capability, b) and c) provide enough coorectionin such away the response could be smooh character orsinusoidal character, and d) the wrong trajectory due totoo much correction capability.

Therefore we have to initiate our basic defense technol-ogy ourshelves, in which we had to create product basedon alternatif strategy, avoiding of further advancement offoreign technology but further strengthen on our basicmilitary technology.In this paper we present the development of basic torpedosteering control using simple controller but the end resultshould have high precision capability.

Fig.1. A torpedo is one of the unmanned underwatervehicles, one of the branch of defence technology

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Fig. 3. Deflection of the rudder (á) as a response of thecontrol system. The rudder produced rudder lift and

rudder drag.

mODELING SOLUTIONS

The torpedo system have the hull, where it had centre ofgravity, centrifugal force and acceleration have taken place.The system, is assumed to have only two-dimensional incharacter, has a rudder into which the resulting controlaction is operated to have movement direction toward theright target

a. The Governing EquationsThe complete system of the forces acting on the torpedovessel at any instant are shown on Fig. 4. The ñ was theinstantaneous radius of curvature of the path. Let the com-ponents of F and R in the direction of the X and Y axesdenoted by the corres subscripts and let the inertia forcesbe denoted as shown, then we have the governing equa-tions (Rossell and Chapman, 1958)

b. For steady turning:

M’ ρv2

isin è + Rx + Fx –T = 0 …………..(4)

- M’cos è + Ry – F cos è = 0 ……. …(5)F.p – Ry.a ‘“ N à0 then Ry = (F.p – N)/a.. (6)

c. The Result Equation for Controlling

From (5) one found- M’cos è + Fcosá = 0then…(8)

and from (4) one found……………….(9)therefore

………..(10)

Fig. 4. The complete system of the forces acting on thevessel at any instant. The applied force were the rudder

force F, hull resistance R, and the propeller thrust T

M’ dtdvi cos è = M’ ρ

v2i

sin è + Rx + Fx –T (1)

M’sin è = - M’cos è + Ry – Fy ..(2)J’= F.p – R.q = N …………………(3)

d. Controlled System

The negative feedback controlled system for the wholetorpedo system are illustrated on Fig. 5. The flow for un-manned underwater vehicle dynamics, such as torpedo,have been modelled as input (á) – output (è) system.In the torpedo vehicles we could use several differenceways to measure current direction (è1) such as using GPSand IMU system. The result of current direction (è1) wouldbe compared with reference direction (è0) and then onecan find the instataneous angle difference “ è

“è = è0 - è1

We use combination of GPS / IMU to determine the cur-rent torpedo direction, by calculating the reference direc-tion between the target reference point and the currenttorpedo location measured by GPS/IMU syatenm.The current torpedo direction are measure as the tangentline of current trajectory. The resulting instantaneous di-

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rection angle differences will be inputed to tha PID con-troller.

d. Sensitivity criteriaThe complete control system using simple PID controllerto control the flow of torpedo dynamics in order to hit thetarget precisely are presented in Fig. 5.The instantaneous direction angle difference (“è)to drivethe PID controller to produce precise rudder deflectionangle (á) have been illustrated in Fig. 6.

Fig. 5. The complete control syatem using simple PIDcontroller to control the flow of UUV dynamics to hit the

Fig. 6. The instataneous direction angle difference drovePID controller to produce precise rudder deflection

angle.

In the system, presented on Fig. 6 or formulated on Eq.(10) contains some functional characteristic of the rudder,F, Fx, Fy, and of the hull drag Rx, Ry which have to besupplied with the actual data.At last some parameter to be adjusted for the PID control-ler, K, ôi, and ôD could solve using Root location, Routhstability criterion, and Hurwitz stability criterion.

CONCLUSIONS

In conclusion, precision targeting for unmanned under-water vehicles such as torpedo using simple controllerhave been designed. It consists of PID controllers ma-nipulating the control surface to get the right directiontoward the target precisely.The process block diagram have to be analyzed using fluidflow dynamics force balances. The resulting fluid dynam-ics for torpedo syatem, the ID controller can be designedand tunned using Routh-Huruwich stability criteriaon.

REFERENCES

Hwang, Oh, Lee, Park, and Rizos (2005) Design of a low-cost attitude determination GPS-INS integrated navigationsyatem, GPS Solution, Vol. 9, pp. 294-311.modeling, GPS Solution, Vol. 10, pp. 1-11.Cohen, JE, Small, C. (1997) Estimates of coastal popula-tions, Science, Vol. 278, pp. 1211-1212Huang, YW and Chiang, KW (2008) An intelligent andtem, GPS Solution, Vol. 12, pp. 135-146.Koh, Lau, Seet, and Low (2006) A Control module Schemefor UUV, J. Intell. Robot System, Vol. 46, pp. 43-45.Rossell, HE and Chapman, LB (1958) Principles of NavalArchitechture, Vol II, New YorkYuh, J (2000) Design and control of Autonomous Under24.

(Footnotes)1

Paper presented on the International Conference on Cre-ative Communication and Innovative Technology 2009 (ICCIT-09), August 8 th , 2009, Jl. Jenderal Sudirman No.40, Tangerang Banten Indonesia.

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Paper

AbstractWebsite is the realization of the internet technology. Nowadays, as seen from the usage trend, the website has evolved. Atthe beginning, website merely adopts the need for searching and browsing information. The initial step of the raise of thiswebsite is often recognized as web 1.0 technology. At present, web 2.0 technology, which enables well web-to-webinteraction, has come. Kinds of interaction such as changing information (sharing), in the form of document (slideshare),picture (flickr), or video (youtube), information exploitation (wikipedia), and also online communities creation (weblog,web forum) are principally a service that involve communities (the core of web 2.0). These matters bring impacts causedby the raise of social interactions in virtual world wide (the internet) that is followed by the appearance of learninginteraction and training anywhere-anytime which is termed as e-Learning. Basically, online learning requires self-learning method and learning habit, which is –unfortunately- possessed by a few Indonesian human resources. Thiscondition is being worst by the present e-Learning system that focuses merely on the delivery process of the same learningsubstance content toward the learner, abandon the cognitive aspect and it does not offer approach or an interactive self-learning experience and also abandon the adaptation aspect of user with the system. Therefore, successful e-Learning inIndonesia needs e-Learning system that applies web 2.0 technology which urges the learner to actively participate andthe system which stresses the personalization such as comprehensive ability, adaptive to levels learner capability andpossessing knowledge resources support.Within the constructed e-Learning system, ontology is going to be applied as the representation of meaning of knowledgeformed by the learner who uses the system.

Keywords: e-Learning, personalization e-Learning, adaptive e-Learning, ontology

Saturday, August 8, 200916:45 - 17:10 Room L-210

Ontology implementation within e-Learning Personalization Systemfor Distance Learning in Indonesia

1. BackgroundThe vast use of internet in the present time by people indeveloped countries and developing countries like Indo-nesia has changed the way of living especially in eachoperational activity. According to Internet World Stat, In-donesian netters reach 20 million up to 2007 and this num-ber is recorded on the list number 14 after Canada. Internethas changed the paradigm of place and distance that ispreviously seemed far to be nearer. Therefore the use isbadly needed in Indonesia that geographically has thou-sands island.

Web site is the realization of the internet. As seen from theusage trend up to now the web has evolved. At the begin-ning, website merely adopts the need for searching andbrowsing information. The initial step of the raise of thiswebsite is often recognized as web 1.0 technology. Atpresent, web 2.0 technology, which enables well web-to-web interaction, has come. Kinds of interaction such aschanging information (sharing), in the form of document(slideshare), picture (flickr), or video (youtube), informa-tion exploitation (wikipedia), and also online communities

Bernard Renaldy SutejaJurusan Teknik Informatika, Fakultas Teknologi Infomasi UK. Maranatha; [email protected]

Suryo GuritnoIlmu Komputer Universitas Gadjah Mada; [email protected]

Retantyo WardoyoIlmu Komputer Universitas Gadjah Mada; [email protected]

Ahmad AshariElektronika dan Instrumentasi Universitas Gadjah Mada; [email protected]

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creation (weblog, web forum) are principally a service thatinvolves communities (the core of web 2.0). These mattersbring impacts like the increasing number of social interac-tions in virtual world wide (the internet) that is followed bythe appearance of learning interaction and training any-where-anytime which is termed as e-Learning.

The development of e-Learning itself has suc-cessfully dragged the attention of many parties like indus-try and education. The existence of e-Learning in industryhas increased employees’ competency. For instance,Mandiri Bank has launched Learning Management Sys-tem (LMS) to train about 18 thousand employees spreadover 700 branches (Swa Magazine, 2003). To add, CISCO,PT SAP Indonesia, PT Telekomunikasi Indonesia and IBMIndonesia have applied e-learning system to develop theirhuman resources (Sanjay Bharwani, 2004). As well as ineducation, e-Learning has given the change point of viewfor teaching-learning process. Based on ASTD (AmericanSociety for Training and Development) survey result in2004, 90% of US Universities have more than 10.000 stu-dents who use e-Learning. While in business, the percent-age reaches 60% (Ryann Ellis, 2004).

Simply, e-Learning in education is a process ofteaching-learning through a computer connected to theinternet, which all facilities provided in the learning venueare functionally changeable with certain applications.Learning substances are downloadable, while interactionbetween teacher and students in the form of assigningtasks can be done intensively in the form of discussion orvideo conference.

In Indonesia the regulation from government orrelated department to support the realization of e-Learningfor education is implied in Decree no.20 Year 2003 aboutNational Education System clause 31 and the NationalEducation Minister’s Decree and Act no. 107/U/2001 aboutPTJJ, specifically permits education manager in Indonesiato manage education through PTJJ by using IT.

Similar to e-Learning development, vendors ofsystem development appear, starting from open sourcebased system such as Moodle, Dokeos, Sakai etc to pro-prietary like Blackboard (Web CT). The vast developmentof open source based system is due to the small amount ofe-Learning system investment. The investment includeshardware and software if it is compared to learning con-ventionally. To mention, several universities in Indonesiaand overseas have applied this e-Learning system.

Yet, it is not a guarantee that the increasing num-ber of e-Learning system supports the learning transfor-mation or learning application itself. In 2000, a study heldby Forrester Group showed that 68% refused the e-Learn-

ing training concept. Meanwhile, the other study indicatedthat from all registered e-Learning participant 50%-80%did not accomplish the training (Delilo, 2000). It is similarwith e-Learning system application in Indonesia. The worstthing is mostly established e-Learning systems are unus-able at the end. Basically, online learning requires self-learning method and learning habit, which is –unfortu-nately- possessed by a few Indonesian human resourcesonly. This condition is being worst by the present e-Learn-ing system that focuses merely on the delivery process ofthe same learning substance content toward the existinglearner, abandon the cognitive aspect and it does not offerapproach or an interactive self-learning experience and alsoabandon the adaptation aspect of user with the system.Therefore, successful e-Learning in Indonesia needs e-Learning system that applies web 2.0 technology whichurges the learner to actively participate and supported bythe system which stresses on the personalization such ascomprehensive ability, adaptive to levels of learner’s ca-pability and possessing knowledge resources support.

Online learning that needs self-learning methodand habit to learn will be realized into an e-Learning sys-tem by using web 2.0 technology (wiki, blog, flickr, andyoutube) which focuses on the communities employedservice. Content learning will be collected from knowledgeresources web 2.0 based in which the metadata is man-aged using pedagogy ontology.

Ontology, a knowledge representation on aknowledge base that is formed later, is used as a part to-ward system user in the formed social network. To sum up,e-Learning system that stresses on the personalization suchas the ability to accommodate cognitive aspect of the user,understandable and adaptive toward various users -at theend- is capable to increase learner motivation of e-Learn-ing system user.

2. Theoretical Review

2.1. e-Learning and ContentElectronic learning or e-Learning is a self-learn-

ing process facilitated and supported through the use ofICT [1]. Generally, from the developing e-Learning systemnowadays, e-Learning –based on the interactivity- is clas-sified into 2 groups:

• Static learning. The system user can download theneeded learning substance only (content). While theadminis trator can upload substance files only. The actual learning situation like communication is absent onthis system. The system is useful for those who canlearn by themselves from readers supplied on the system in the form of HTML, Power Point, PDF, or video. If

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it is used, the system can functionally support teaching-learning activities done face-to-face in class.

• Dynamic learning. The facilities offered are more varyfrom the first system mentioned. The facilities such asdiscussion forum, chatting, e-mail, learning evaluationtools, user management and electronic substance management are available. These enable the user (students)to learn in a learning environment that similar to classroom situation. This second system can be used to helptransformation process of learning paradigm fromteacher-centered to student-centered. It is no longerthe instructor who actively delivers the substance orrequest students to ask about indigestible substancebut, here, the students are trained to learn critically andactively. E-Learning system which is developed mayuse collaborative learning method approach (collaborative learning) or learning from the process of the givenproblem solving (problem-based learning).

The relation between learning condition and ap-propriate facilities can be seen in the table below (adoptedfrom Distance Learning and SunMicrosystems [2]) :

Picture 1. Perbandingan distance learning

e-Learning content is any digital resources which is usedto support learning process. E-Learning content can becategorized into 2 parts:- textual, including text based content like plain-text and PDF- non-textual, including multimedia content such as audio, visual and animation

Textual content can be found easily throughsearch engine (like Google or Yahoo) by typing the key-words. This can be done by a skillful person only to gainthe needed content from some found content results andthen to be combined. Non-textual is not so simple. It ishard to find although the person has used search engine.

Personalization is the next step of e-Learning evo-lution. According to Paulo Gomes et el, learner may feelvarious cognitive style and create efficiency within theproper use of e-Learning system for different backgroundand capability level. There are two personalization mod-els: on-line personalization (picture 2), observe student

interaction through the system continuously within realtime, always giving the appropriate substance content(Paulo, 2006).).

Picture 2. On-line personalization model

Off-line personalization (picture 3), walked by combiningprovided students data that is analyzed later to gain coursecontent change recommendation..

Picture 3. Off-line personalization model

The appearance of Web Semantic technology, Meta datamay be added into e-Learning content (including

pedagogy attributes) and later be organized into ontol-ogy, so it will be easier in distribution, discovery and thecontent use in such a better way. Through this way, it snot only human can easily find and organized needed con-tent but also smart agent. Smart agent in the applicationwill find and organize the content from heterogenic con-tent source and then combine them to be customizedcourseware with specific criteria and other rules. This cus-tomized courseware refers to groups of content (sourcedfrom heterogenic content) where related content and peda-gogy are supported (Renaldy and Azhari, 2008)

2.2. e-Learning standardizationThere is e-Leaning standardization that must be

used as a reference of system development:2.2.1. LTSCIt is invented by Institute of Electrical and Electronic Engi-neers (IEEE) that has created many standard of technol-ogy for electrical, Information Technology and Science.The aim of LSC is to form accreditation of technical stan-dard, giving training recommendation, and a reference inlearning technology.

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2.2.2. IMSIMS is an important organization in e-Learning com-

munity since consortium among academic institution, com-pany and government to build and support open specifi-cation for learning distribution and content developmentand also student exchange among the different systems.

2.2.3. ADLADL create Shareable Courseware Object Reference

Model (SCORM). SCORM is a standard specification forreusability and interoperability from learning content [7].

SCORM focuses on to two important aspects ininteroperability from learning content:

- Defining the model aggregately to wrap learningcontent

- Defining API which is usable for communicationbetween learning content and the applied systemSCORM divides learning technology based on:

- Learning Management System (LMS)Shareable Content Object (SCOs)-

Picture 4. Component of SCROM 1.2.

There are many tools to use SCORM like eXe-Learning.

Picture 5. Penggunaan SCROM pada eXe-LearningThe use on e-Learning has been supported, for example e-Learning opensource Moodle.

Picture 6. Implementasi SCROM pada Moodle

2.3. Semantic Web TechnologySemantic web is the development of the next web

generation or commonly termed as the evolution of WWW(World Wide Web) issued in 2002. Semantic web is de-fined as groups of technology, in which it enables thecomputer to comprehend the meaning of information basedon metadata, namely the information of the content. Withthe existence of metadata computer is expected to trans-late the input so the result will be displayed more detailand exact. W3C (World Wide Web Consortium) that de-fine metadata format is Resource Description Format (RDF).Each unit of RDF has 3 composition namely subject, predi-cate and object. Subject and object are entities showed bythe text. While predicate is the composition that explainsubject point of view which is explained by the object. Themost interesting thing from RDF is an object can be a sub-ject which is explained later by another object. So, objector input can be explained clearly, in detail, and appropriatewith the user’s will, who give the input.

In order to reach the goal, it is necessary to givemeaning into each content (as attributes) which will beused by web semantic technology into several layers:

Picture 7. Layer Web Semantik

- XML Layer, represents the data- RDF Layer, represents the meaning of data- Ontology Layer, represents general form of rules/deals

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about meaning of data- Logic Layer, applies intelligent reasoning with mean

ingful data.

Semantic web technology can be used to buildsystem by collecting e-Learning content from differentsource to be processed, organized and shared to users orartificial agent by using ontology. There are three impor-tant technology involved in the use of semantic webnamely: eXtensible Markup Language (XML), ResourceDescription Framework (RDF), and Ontology Web Lan-guage (OWL).

2.3. Ontology WebOntology has many definitions as explained on

certain sources including what is revealed by scientist.Neches et el gives the first definition about ontology; Anontology is a definition from a basic understanding andvocabulary relation of an area as a rule from terminologycombination and relation to define vocabulary.

Gruber’s definition that is mostly used by people is, “On-tology is an explicit specification of conceptualism.”Barnaras, on CACTUS project, defines ontology based onits development. The definition is, “ontology gives under-standing for explicit explanation of concept toward knowl-edge representation on a knowledge base” [5].There is a book that defines ontology; one of them is “TheSemantic Web”. It defines ontology as:

1) A branch of metaphysic that focuses on natureand relationship among living creature

2) A theory of living creature’s instinctOntology is a theory of the meaning of an object, aproperty of an object, and its relation which may occuron a knowledge domain. From a philosophy point ofview, ontology is a study of an exist thing. Besides,ontology is a concept that systematically explains aboutany real/exist thing. In a field of Artificial Intelligent(AI), ontology has 2 related definitions. First, ontology is a representation of vocabulary which is specialized for domain or certain subject discussion. Second,ontology is a body of knowledge to explain a certaindiscussion. Generally, ontology is used on ArtificialIntelligent