International Conference On Mathematics, Geometry

International Conference On Mathematics, Geometry, Statistics, and Computation

IC-MaGeStiC 2021


IC-MaGeStiC 2021

i

International Conference

On Mathematics, Geometry, Statistics, and

Computation (IC-MaGeStiC) 2021

Saturday, November 27th 2021

Universitas Jember, East Java, Indonesia

OVERVIEW of IC-MaGeStiC

The Mathematics Department of Jember University invites you to participate in The

International Conference on Mathematics, Geometry, Statistics, and Computation (IC-

MaGeStiC) which will be held on November 27th, 2021. Due to the existence of

COVID-19, IC-MaGeStiC 2021 will be a full virtual conference, so besides the full

paper, participants are also strongly advised to send a video presentation for

anticipating interference on the internet network. This conference is an excellent forum

for participants to exchange findings and research ideas on mathematics and science

education and to build networks for further collaboration.

SCOPES

1. Mathematical Physics

2. Computational Physics

3. Statistical Physics

4. Geomathematics and Geophysics

5. Mathematical Methods in Physics

6. Artificial Intelligences

7. Data Mining & Applications

8. Combinatorics, Graph Theory and Applications

9. Image and Signal Processing

10. Mathematical Modelling

11. Numerical Methods and Analysis

12. Operations Research and Optimization

13. Applied and Theoretical Algebra

14. Applied and Theoretical Statistics

15. Mathematics Education

16. Data Sciences and Data Security

Organizer

DEPARTMENT OF MATHEMATICS

FACULTY OF MATHEMATICS AND NATURAL SCIENCES

UNIVERSITY OF JEMBER


IC-MaGeStiC 2021

ii

Committees

Chairman

Dr. Kiswara Agung Santoso, S.Si., M.Kom. (University of Jember, Indonesia)

Organizing Committee

1. Kusbudiono, S.Si., M.Si. (University of Jember, Indonesia)

2. Abduh Riski, S.Si., M.Si. (University of Jember, Indonesia)

3. Ikhsanul Halikin, S.Pd., M.Si. (University of Jember, Indonesia)

4. Drs. Moh. Hasan, M.Sc., Ph.D. (University of Jember, Indonesia)

5. Dr. Novi Herawati Bong (University of Delaware,United States)

6. Dr. Kristiana Wijaya, S.Si., M.Si. (University of Jember, Indonesia)

7. Inne Singgih, Ph.D. (University of Cincinnati, United States)

8. Natanael Karjanto, Ph.D. (Sungkyunkwan University, South Korea)

9. Prof. Drs. I Made Tirta, M.Sc., Ph.D. (University of Jember, Indonesia)

10. Dr. Alfian Futuhul Hadi, S.Si., M.Si. (University of Jember, Indonesia)

11. Dr. Firdaus Ubaidillah, S.Si., M.Si. (University of Jember, Indonesia)

12. Dr. Agustina Pradjaningsih, S.Si., M.Si. (University of Jember, Indonesia)

13. Dr. Yuliani Setia Dewi, S.Si., M.Si. (University of Jember, Indonesia)

14. Dr. Mohamat Fatekurohman, S.Si., M.Si. (University of Jember, Indonesia)

15. M. Ziaul Arif , S.Si., M.Sc. (University of Eastern Finland, Finland)

16. Kosala Dwidja Purnomo, S.Si., M.Si. (University of Jember, Indonesia)

17. Ahmad Kamsyakawuni, S.Si., M.Kom. (University of Jember, Indonesia)

18. Dian Anggraeni, S.Si., M.Si. (University of Jember, Indonesia)

19. Bagus Juliyanto, S.Si., M.Si. (University of Jember, Indonesia)

20. Millatuz Zahroh, S.Pd., M.Sc. (University of Jember, Indonesia)

21. Yoyok Yulianto (University of Jember, Indonesia)

22. Yulihantoro (University of Jember, Indonesia)

23. Sabar Yulianto (University of Jember, Indonesia)

24. Ayu Rosida (University of Jember, Indonesia)

25. Niken Sayekti Megawati (University of Jember, Indonesia)

26. Irma Dwi Anggraeni (University of Jember, Indonesia)

27. Dimas Maulana Kamal Putra (University of Jember, Indonesia)

28. Ulfah Izzatur Rofiah (University of Jember, Indonesia)

29. Renata Wijayanti (University of Jember, Indonesia)

30. Lailatur Robi'ah (University of Jember, Indonesia)

31. Nur Halimatus Sa'diyah (University of Jember, Indonesia)

32. Hikmatul Mauluda (University of Jember, Indonesia)

33. Evita Figur Anggraheni (University of Jember, Indonesia)

34. Muhammad Ali Musa (University of Jember, Indonesia)

35. Yoggy Harisusilo Putra (University of Jember, Indonesia)

36. Sella Septiana (University of Jember, Indonesia)

37. Nina Almira Azaria (University of Jember, Indonesia)

38. Himpunan Mahasiswa Matematika (HIMATIKA), University of Jember, Indonesia


IC-MaGeStiC 2021

iii

Foreword by the Chairman of IC-MaGeStiC 2021

On behalf of the organizing committee, we are honored and delighted to welcome

you to the International Conference on Mathematics, Geometry, Statistics, and

Computation (IC-MaGeStiC) 2021. This conference is the first international conference

we are holding. Therefore, we deeply apologize if there are things that have not been

carried out optimally.

In this occasion, due to the COVID 19 pandemic, the IC-MaGeStiC 2021 is held

online on November 27th, 2021. The IC-MaGeStiC 2021 is aimed to bring together

scholars, leading researchers, and experts from diverse backgrounds and application

areas in science. Special emphasize is placed on promoting interaction between the

science theoretical, experimental, and other topic related to the mathematics.

At IC-MaGeStiC 2021, there are five keynote speakers and six invited speakers as

listed below.

1. Prof. Nobuaki Obata, Tohoku University Japan

2. Martianus Frederic Ezerman Ph.D., Nanyang Technological University Singapore

3. Fernando Marmolejo-Ramos, Ph.D., University of Eastern Finland

4. Prof. Marko Vauhkonen, Ph.D., University of South Australia

5. Natanael Karjanto Ph.D., Sungkyunkwan University, Republic of Korea

6. Prof. Indah Emilia Wijayanti, Universitas Gadjah Mada, Indonesia

7. Dr. Erry Hidayanto, University of Malang, Indonesia

8. Prof. I Made Tirta, University of Jember, Indonesia

9. Moh. Hasan, Ph.D., University of Jember, Indonesia

10. Dr. Kristiana Wijaya, University of Jember Indonesia

While for the conference participants, there are 65 participants which has been

submitted abstract via the easy chair conference system. Then, the 60 full papers have

been submitted from the participant. The papers will be presented to 6 parallel sessions

in the IC-MaGeStiC 2021. And then for the final decision, the selected papers will be

published to proceeding to Atlantis Press indexed by Web of Science (WoS) and the

rest will be published in e-proceedings with an ISBN. We deeply thank the authors for

their enthusiastic and high-grade contribution.

The IC-MaGeStiC 2021 would not be possible running without the dedicated

efforts of many people especially all organizing committee members who have worked

hard with us in planning and organizing the programs. We are grateful to volunteers

who contributed to the various processes that make up the conference and it would not

be possible for me to name them all in this short message.

I hope that during this conference you find the conference fulfilling and enjoyable.

Dr. Kiswara Agung Santoso, S.Si., M.Kom.

Chairman of IC-MaGeStiC 2021

Mathematics Department, University of Jember


IC-MaGeStiC 2021

iv

TABLE OF CONTENTS

Committees ...................................................................................................... ii

Foreword by the Chairman of IC-MaGeStiC 2021 ....................................... iii

Table of Contents ............................................................................................ iv

Rules of Plenary Session ................................................................................. viii

Rules of Parallel Session ................................................................................. ix

Schedule........................................................................................................... 1

Room 1: Computation..................................................................................... 2

Room 2: Graph and Algebra 1 ....................................................................... 3

Room 3: Graph and Algebra 2 ....................................................................... 4

Room 4: Modeling and Analysis ..................................................................... 5

Room 5: Statistics 1 ......................................................................................... 6

Room 6: Statistics 2 and Education ................................................................ 7

Keynote and Invited Speaker Abstracts......................................................... 8

Quadratic Embedding Constants of Graphs ....................................................... 9

Holographic Sensing ......................................................................................... 10

Electromagnetic Flow Tomography .................................................................. 11

On Ramsey minimal graphs for a 3-matching versus a path on five vertices ...... 12

Two Dimensional Dynamics Simulation of Depositing Granular Materials ....... 13

Polynomial Rings in Post-quantum Cryptography System ................................. 14

Statistical modeling of high zero and heavily right skewed continuous responses

using GAMLSS ................................................................................................ 15

Why is it so hard to get them talking? ............................................................... 16

Mathematical Thinking in Mathematics Learning and Research Related to

Mathematical Thinking ..................................................................................... 17

Presenter of Parallel Session Abstracts .......................................................... 18


IC-MaGeStiC 2021

v

Pattern Recognition Of Batik Madura Using Backpropagation Algorithm ......... 19

A Modification of ECDSA to Avoid the Rho Method Attack ............................ 20

DOPE: MDC-2 scheme based on PRESENT algorithm ..................................... 21

Modification Interior Point Method For Solving Interval Linear Programming . 22

Classification the Melon Rinds Using Convolutional Neural Network ............... 23

Image Authentication Using Magic Square ....................................................... 24

Solving Fully Fuzzy Linear Equations System Using Metaheuristic Algorithm . 25

An Aplication Of Hybrid Cat-Particle Swarm Optimization Algorithm: Modified

Bounded Knapsack Problem With Multiple Constrains ..................................... 26

Implementation Of Hill Cipher Invers Matrix And Cryptography In Primary Key

Registration Process On A New Student Admission Site Mandala High School Of

Economic Sciences ........................................................................................... 27

Learning Materials Development of Parametric Curves and Surfaces for Modeling

the Objects Using Maple on Differential Geometry Courses.............................. 28

Ramsey Graphs for A Star On Three Vertices versus A Cycle ........................... 29

L(2,1)-Labeling of Lollipop and Pendulum Graphs ........................................... 30

Gerschgorin Disc Theorem and Its Application ................................................. 31

Modular Irregularity Strenght of Generalized Dodecahedral Graphs.................. 32

Labelling Friendship and Windmill Graphs with a Condition at Distance Two .. 33

Spectrum of Unicyclic Graph ............................................................................ 34

Further Result of H-Supermagic Labeling for Comb Product of Graphs ............ 35

A Minimum Coprime Number for Amalgamations of Wheel ............................ 36

Prime-Order Cayley Graph of Dihedral Group .................................................. 37

Distinguishing Number and Partition Dimension of Generalized Theta Graph... 38

Application of Gröbner Bases in Ideal Membership Problem of Polynomial Ring

k[x1, ..., xn] ........................................................................................................ 39

Edge Magic Total Labeling of (n,t)-kites ........................................................... 40

Rainbow Connection Number of Shackle Graphs .............................................. 41

Implementations of Dijkstra Algorithm for Searching The Shortest Route of Ojek

Online and a Fuzzy Inference System for Setting the Fare Based on Distance and

Difficulty of Terrain (Case Study: in Semarang City, Indonesia) ....................... 42


IC-MaGeStiC 2021

vi

Local Antimagic Chromatic Number of Firecracker Graph ............................... 43

On Ramsey (3K_2,P_4)-minimal Graphs .......................................................... 44

Local Antimagic Vertex Coloring of Amal(S_(m+1),S_(n+1)) .......................... 45

Local Antimagic Vertex Coloring of Gear Graph .............................................. 46

Local Antimagic Vertex Coloring for Corona Product of Graph Pn o Pk ............ 47

Rainbow (Vertex) Connection Numbers of Bat Graphs and Covid Graphs ........ 48

Magic and Antimagic Decomposition of Amalgamation of Cycles .................... 49

On The Minimum Span of Cone, Tadpole, and Barbell Graphs ......................... 50

Hurdle Regression Modelling on The Number of Deaths from Chronic Filariasis

Cases in Indonesia ............................................................................................ 51

Bayesian Statistical Modeling Perspective in the Covid-19 Disaster Mitigation

Series in East Java Region................................................................................. 52

High Order Three-Steps Newton Raphson-like scheme for Solving Nonlinear

Equation Systems .............................................................................................. 53

Root Water Uptake Process for Different Types of Soil in Unsteady Infiltration

from Periodic Trapezoidal Channels ................................................................. 54

Analysis of Factors Affecting the Depth of Poverty Index in Papua Province

Using Panel Data Regression ............................................................................ 55

A Mathematical Model for COVID-19 to Predict Daily Cases using Time Series

Auto Regressive Integrated Moving Average (ARIMA) Model in Delhi Region, India 56

Symmetry Functions With Respect To Some Point in Rn and Their Properties .. 57

Hanging Rotera Modeling by Joining Deformation Result of Space Geometry Objects 58

Generalization of Chaos Game on Polygon ....................................................... 59

Information Retrieval Using The Matrix Method (Case Studi: Three Popular Online

News Sites In Indonesia) ................................................................................ 60

The application of the Bayesian framework in the joint reconstruction of conducti-

vity and velocity of two-phase flows problems by using dual-modality ............. 61

Diabetes Mellitus Screening Model Using Fuzzy K-Nearest Neighbor in Every

Class Algorithm ................................................................................................ 62

Bayesian Accelerated Failure Time Model and its Application to Preeclampsia 63


IC-MaGeStiC 2021

vii

Multiple Discriminant Analysis Altman Z-Score, Multiple Discriminant Analysis

Stepwise and K-Means Cluster for Classification of Financial Distress Status in

Manufacturing Companies Listed on the Indonesia Stock Exchange in 2019 ..... 64

Double Bootstrap Method for Autocorrelated Data in Process Control .............. 65

Generalized Space Time Autoregressive-X (Gstar-X) Model In Forecetting

Cabbage Production In Malang ......................................................................... 66

Contact Tracking with Social Network Analysis Graph ..................................... 67

Projection Pursuit Regression on Statistical Downscaling using Artificial Neural

Network and Support Vector Regression Methods ............................................ 68

Correlation Analysis between the Number of Confirmed Cases of COVID-19 and

Stock Trading in Indonesia................................................................................ 69

Application of Structural Equation Modelling (SEM) in Analysis of Performance

Determinants of Multipurpose Cooperatives (KSU) in Jembrana Regency of Bali

of Indonesia ...................................................................................................... 70

Random Semi Under Sampling to Increase The Sensitivity of Imbalanced Data

Classification with Binary Logistic Regression ................................................. 71

Competing Risk Model for Prediction of Preeclampsia ..................................... 72

Analysis of Students’ Mathematical Deductive Reasoning Skill ........................ 73

The Vector Time Series Analysis on COVID-19 Cases in Bandung City of West Java 74

SHINY OFFICE-R: a Web-based Data Mining Tool for Exploring and Visualizing

Company Profiles ............................................................................................. 75

Naive Bayes Classifier (NBC) For Forecasting Rainfall In Banyuwangi District

Using Projection Pursuit Regression (PPR) Method .......................................... 76

Statistical Downscaling Technique Using Response Based Unit Segmentation-Partial

Least Square (REBUS-PLS) for Monthly Rainfall Forecasting .......................... 78

Statistical Literacy Ability In Term Of Adversity Quotient ............................... 80

Learning Content Development in Modeling Creative Industry Objects Using Real

Function Formulas Supported with Maple ......................................................... 81

Investigating Difficult Concepts in Problem Absolute Value Function for Prospective

Teachers ........................................................................................................... 80

Weather Forecasting at BMKG Office, Lumajang City Using Markov Chain Method 81

Means-Ends-Analysis Model with Didactical Engineering to Enhance Junior High

School Students’ HOTS Ability and Mathematical Habits of Mind ................... 82


IC-MaGeStiC 2021

viii

Comparison of Kriging and Neural Network Methods in Interpolation of Rainfall 82

Classification of Bank Deposits Using Naive Bayes Classifier (Nbc) and K–Nearest

Neighbor (K-Nn)............................................................................................... 83


IC-MaGeStiC 2021

ix

Rules of Plenary Session

Please join Zoom 15 minutes before the event starts.

Participants are expected to turn off the sound (mute) during the Conference process

All participants who take part in the Conference through Zoom can ask questions by:

raise your hand or Type QUESTION, then proceed with writing the name, origin

of the agency and the question briefly. The moderator will ask the speaker a number

of questions because the time for discussion is limited.

Certificates will be distributed to participants who took part in the event and present the

manuscript.


IC-MaGeStiC 2021

x

Rules of Parallel Session

Please join Zoom 15 minutes before the event starts.

One presentation is allocated 12 minutes, with 8 minutes for the presentation and 4

minutes for the Question & Answer session.

Session chairs need to strictly control the start and closing times of each session.

During your presentation, the session chair will give you notification via zoom chat two

times (indicating that your time allocation is coming to an end)

First notification: THREE minutes presentation time remaining

Second notification: time is over; finish your sentence and STOP your

presentation

The Question & Answer session:

Participants give questions through chat that will be read by chair or directly

unmute your microphone. But please ask permission the Chair first.

If there is some trouble with the connection or the technical from the presenters, it will

be skipped and will be continued by the next presenter. The skipped presenter can

present the manuscript at the end of each session in each room.


IC-MaGeStiC 2021

1

Schedule, Saturday, November 27th 2021

PLENARY SESSION

Time

(GMT+7) Activity Room Chairperson

08.00 – 08.10

Opening Session

Profile of Mathematics Departement

National Anthem

Praying Session

https://unej.id/magestic

Meeting ID: 984 3031

8330 MC

08.10– 08.20 Welcoming Speech

Chairman of MaGeStiC MC

08.20 – 08.30 Opening Speech

Dean of MIPA, University of Jember MC

08.30 – 09.25 Keynote Speaker 1

Prof. Nobuaki Obata Moderator


Martianus Frederic Ezerman, Ph.D. Moderator


Fernando Marmolejo-Ramos, Ph.D. Moderator

11.30– 12.25 Keynote Speaker 4

Prof. Marko Vauhkonen, Ph.D Moderator

12.25 – 13.00 Break Commitee

PARALLEL SESSION

Time

(GMT+7) Paralel Room Room Invited Speaker

13.00 – 16.30 Room 1

Computation

https://unej.id/magesticroom1

Meeting ID: 929 9379 5042

Natanael Karjanto,

Ph.D.

13.00– 16.30 Room 2

Graph & Algebra 1


Meeting ID: 919 2780 4640

Prof. Indah Emilia

Wijayanti

13.00– 16.30 Room 3

Graph & Algebra 2


Meeting ID: 945 0240 3708 Dr. Kristiana Wijaya

13.00– 16.30 Room 4

Modeling & Analysis


Meeting ID: 926 4817 5760 Moh. Hasan, Ph.D.

13.00– 16.30 Room 5

Statistics 1


Meeting ID: 919 9415 2792 Prof. I Made Tirta

13.00– 16.30

Room 6

Math Education &

Statistics 2


Meeting ID: 914 0681 0158 Dr. Erry Hidayanto

https://unej.id/magestic








IC-MaGeStiC 2021

2

ROOM 1

COMPUTATION

Chair Person: Moderator

No Time Presenter Title

1 13.00-13.30 Nathanael Karjanto Why is it so hard to get them talking?

2 13.30-13.45 Abduh Riski Pattern Recognition Of Batik Madura

Using Backpropagation Algorithm

3 13.45-14.00 Amira Zahra A Modification of ECDSA to Avoid the

Rho Method Attack

4 14.00-14.15 Anjeli Lutfiani DOPE: MDC-2 scheme based on

PRESENT algorithm

5 14.15-14.30 Agustina

Pradjaningsih

Modification Interior Point Method For

Solving Interval Linear Programming

6 14.30-14.45 Fauzan Masykur Classification the Melon Rinds Using

Convolutional Neural Network

7 14.45-15.00 Maulidyah Lailatun

Najah

Image Authentication Using Magic

Square

8 15.00-15.15 Merysa Puspita Sari Solving Fully Fuzzy Linear Equations

System Using Metaheuristic Algorithm

9 15.15-15.30 Kiswara Santoso

An Aplication Of Hybrid Cat-Particle

Swarm Optimization Algorithm:

Modified Bounded Knapsack Problem

With Multiple Constrains

10 15.30-15.45 Muhamat Abdul

Rohim

Implementation Of Hill Cipher Invers

Matrix And Cryptography In Primary

Key Registration Process On A New

Student Admission Site Mandala High

School Of Economic Sciences

11 15.45-16.00 Abduh Riski

Learning Materials Development of

Parametric Curves and Surfaces for

Modeling the Objects Using Maple on

Differential Geometry Courses


IC-MaGeStiC 2021

3

ROOM 2

GRAPH & ALGEBRA 1



1 13.00-13.30 Indah Emilia

Wijayanti

Polynomial Rings in Post-quantum

Cryptography System

2 13.30-13.45 Johanes Irsan

Application of Gröbner Bases in

Ideal Membership Problem of

Polynomial Ring k[x1, ..., xn]

3 13.45-14.00 Alfi Y. Zakiyyah Gerschgorin Disc Theorem and Its

Application

4 14.00-14.15 Budi Rahadjeng Spectrum of Unicyclic Graph

5 14.15-14.30 Kusbudiono L(2,1)-Labeling of Lollipop and

Pendulum Graphs

6 14.30-14.45 Ikhsanul Halikin

Labelling Friendship and Windmill

Graphs with a Condition at Distance

Two

7 14.45-15.00 I Putu Putra Gemilang

Adi Guna

Modular Irregularity Strenght of

Generalized Dodecahedral Graphs

8 15.00-15.15 Ganesha Lapenangga

Putra

Further Result of H-Supermagic

Labeling for Comb Product of

Graphs

9 15.15-15.30 Hafif Komarullah A Minimum Coprime Number for

Amalgamations of Wheel

10 15.30-15.45 Ridho Surya Perkasa Prime-Order Cayley Graph of

Dihedral Group

11 15.45-16.00 Andi Pujo Rahadi

Distinguishing Number and Partition

Dimension of Generalized Theta

Graph

12 16.00-16.15 Ikhsanul Halikin On The Minimum Span of Cone,

Tadpole, and Barbell Graphs


IC-MaGeStiC 2021

4

ROOM 3

GRAPH & ALGEBRA 2



1 13.00-13.30 Kristiana Wijaya On Ramsey minimal graphs for a 3-

matching versus a path on five vertices

2 13.30-13.45 Inne Singgih Edge Magic Total Labeling of (n,t)-

kites

3 13.45-14.00 Maya Nabila Ramsey Graphs for A Star On Three

Vertices versus A Cycle

4 14.00-14.15 Asep Iqbal Taufik

Disconnected Graphs in R(3K2,P4)

and Subdivision of Graphs in

R(3K2,P5)

5 14.15-14.30 Amelia Nurannisa

Hadi

Local Antimagic Vertex Coloring of

Amal(S_(m+1),S_(n+1))

6 14.30-14.45 Masdaria Natalina

Br Silitonga


Gear Graph

7 14.45-15.00 Setiawan Local Antimagic Vertex Coloring for

Corona Product of Graph Pn o Pk

8 15.00-15.15 M. Ali Hasan Rainbow Connection Number of

Shackle Graphs

9 15.15-15.30 Suci Yefri Fadhilah

Rainbow (Vertex) Connection

Numbers of Bat Graphs and Covid

Graphs

10 15.30-15.45 Sigit Pancahayani Magic and Antimagic Decomposition

of Amalgamation of Cycles

11 15.45-16.00 Lulu Tasya Ismayah Local Antimagic Chromatic Number

of Firecracker Graph

12 16.00-16.15 Vani Natali Christie

Sebayang

Implementations of Dijkstra Algorithm

for Searching The Shortest Route of

Ojek Online and a Fuzzy Inference

System for Setting the Fare Based on

Distance and Difficulty of Terrain

(Case Study: in Semarang City,

Indonesia)


IC-MaGeStiC 2021

5

ROOM 4

MODELING AND ANALYSIS



1 13.00-13.30 Moh. Hasan Two Dimensional Dynamics Simulation of

Depositing Granular Materials

2 13.30-13.45 Nur Kamilah

Sa'diyah

Hurdle Regression Modelling on The Number

of Deaths from Chronic Filariasis Cases in

Indonesia

3 13.45-14.00 Ani Budi Astuti

Bayesian Statistical Modeling Perspective in

the Covid-19 Disaster Mitigation Series in

East Java Region

4 14.00-14.15 M. Ziaul Arif

High Order Three-Steps Newton Raphson-

like scheme for Solving Nonlinear Equation

Systems

5 14.15-14.30 Millatuz

Zahroh

Root Water Uptake Process for Different

Types of Soil in Unsteady Infiltration from

Periodic Trapezoidal Channels

6 14.30-14.45 Rufina Indriani

Analysis of Factors Affecting the Depth of

Poverty Index in Papua Province Using Panel

Data Regression

7 14.45-15.00 Tarunima

Agarwal

A Mathematical Model for COVID-19 to

Predict Daily Cases using Time Series Auto

Regressive Integrated Moving Average

(ARIMA) Model in Delhi Region, India

8 15.00-15.15 Firdaus

Ubaidillah

Symmetry Functions With Respect To Some

Point in R^n and Their Properties

9 15.15-15.30 Bagus Juliyanto

Hanging Rotera Modeling by Joining

Deformation Result of Space Geometry

Objects

10 15.30-15.45 Kosala Generalization of Chaos Game on Polygon

11 15.45-16.00 Ferry Wiranto

Approach to Getting Relevant Documents

Using The Matric Method Case Studies:

Three Online News Sites in Indonesia

(tribunnews.com, detik.com, and

liputan6.com)

12 16.00-16.15 M. Ziaul Arif

The application of the Bayesian framework in

the joint reconstruction of conductivity and

velocity of two-phase flows problems by

using dual-modality


IC-MaGeStiC 2021

6

ROOM 5

STATISTICS 1



1 13.00-13.30 I Made Tirta

Statistical modeling of high zero and

heavily right skewed continuous

responses using GAMLSS

2 13.30-13.45 Maizarul Ulfanita

Diabetes Mellitus Screening Model

Using Fuzzy K-Nearest Neighbor in

Every Class Algorithm

3 13.45-14.00 Dennis Alexander

Bayesian Accelerated Failure Time

Model and its Application to

Preeclampsia

4 14.00-14.15 Hazrina Ishmah

Multiple Discriminant Analysis Altman

Z-Score, Multiple Discriminant Analysis

Stepwise and K-Means Cluster for

Classification of Financial Distress Status

in Manufacturing Companies Listed on

the Indonesia Stock Exchange in 2019

5 14.15-14.30 Jauharin Insiyah Double Bootstrap Method for

Autocorrelated Data in Process Control

6 14.30-14.45 Lely Holida

Generalized Space Time Autoregressive-

X (Gstar-X) Model In Forecetting

Cabbage Production In Malang

7 14.45-15.00 Alvida Mustika

Rukmi

Contact Tracking with Social Network

Analysis Graph

8 15.00-15.15 Alfian Futuhul Hadi

Projection Pursuit Regression on

Statistical Downscaling using Artificial

Neural Network and Support Vector

Regression Methods

9 15.15-15.30 Dinagusti

Magdalena Sianturi

Correlation Analysis between the

Number of Confirmed Cases of COVID-

19 and Stock Trading in Indonesia

10 15.30-15.45 Novi Nur Aini

Comparison of Kriging and Neural

Network Methods in Interpolation of

Rainfall

11 15.45-16.00 Gusti Ngurah Adhi

Wibawa

Random Semi Under Sampling to

Increase The Sensitivity of Imbalanced

Data Classification with Binary Logistic

Regression

12 16.00-16.15 Nadya Devana Competing Risk Model for Prediction of

Preeclampsia


IC-MaGeStiC 2021

7

ROOM 6

STATISTICS 2 AND EDUCATION



1 13.00-13.30 Dr. Erry

Hidayanto

Mathematical Thinking in Mathematics

Learning and Research Related to

Mathematical Thinking

2 13.30-13.45 Uyan Ahmad

Satibi

Analysis of Students’ Mathematical

Deductive Reasoning Skill

3 13.45-14.00 Slamet

Investigating Difficult Concepts in Problem

Absolute Value Function for Prospective

Teachers

4 14.00-14.15 Wahid Umar

Means-Ends-Analysis Model with

Didactical Engineering to Enhance Junior

High School Students’ HOTS Ability and

Mathematical Habits of Mind

5 14.15-14.30 Bagus Juliyanto

Learning Content Development in Modeling

Creative Industry Objects Using Real

Function Formulas Supported with Maple

6 14.30-14.45 I Made Tirta

SHINY OFFICE-R: a Web-based Data

Mining Tool for Exploring and Visualizing

Company Profiles

7 14.45-15.00 Dian Anggraeni

Naive Bayes Classifier (NBC) For

Forecasting Rainfall In Banyuwangi District

Using Projection Pursuit Regression (PPR)

Method

8 15.00-15.15 Izdihar Salsabila

Statistical Downscaling Technique Using

Response Based Unit Segmentation-Partial

Least Square (REBUS-PLS) for Monthly

Rainfall Forecasting

9 15.15-15.30 Iffa Hanifah

Rahman

Statistical Literacy Ability In Term Of

Adversity Quotient

10 15.30-15.45 Utriweni

Mukhaiyar

The Vector Time Series Analysis on

COVID-19 Cases in Bandung City of West

Java

11 15.45-16.00 Ummi Masrurotul

Jannah

Weather Forecasting at BMKG Office,

Lumajang City Using Markov Chain

Method

12 16.00-16.15 Dian Angraeni

Classification of Bank Deposits Using Naive

Bayes Classifier (Nbc) and K–Nearest

Neighbor (K-Nn)


IC-MaGeStiC 2021

8

KEYNOTE AND INVITED

SPEAKER ABSTRACTS


IC-MaGeStiC 2021

9

Quadratic Embedding Constants of Graphs

Nobuaki Obata

Tohoku University, Japan

ABSTRACT

The quadratic embedding (QE) constant of a finite connected graph 𝐺, denoted by

𝑄𝐸𝐶 𝐺 is by definition the maximum of the quadratic function associated to the distance

matrix on a certain sphere of codimension two. Since the QE constant was introduced

by Obata and Zakiyyah in 2018, it has been expected to be a useful invariant of finite

connected graphs for their classification. In this talk I will survey basic results on the

QE constant and propose some questions.


IC-MaGeStiC 2021

10

Holographic Sensing

A. M. Bruckstein a,b, M. F. Ezerman b,∗, A. A. Fahreza b, S. Lingb

aDepartment of Computer Science, Technion, Israel Institute of Technology, Haifa

32000, Israel. bSchool of Physical and Mathematical Sciences, Nanyang Technological University, 21

Nanyang Link, Singapore 637371.

ABSTRACT

Holographic representations of data encode information in packets of equal importance

that enable progressive recovery. The quality of recovered data improves as more and

more packets become available. This progressive recovery of the information is

independent of the order in which packets become available. Such representations are

ideally suited for distributed storage and for the transmission of data packets over

networks with unpredictable delays and or erasures. Several methods for holographic

representations of signals and images have been proposed over the years and multiple

description information theory also deals with such representations. Surprisingly,

however, these methods had not been considered in the classical framework of optimal

least-squares estimation theory, until very recently. We develop a least-squares

approach to the design of holographic representation for stochastic data vectors, relying

on the framework widely used in modeling signals and images.

Keywords: cyclostationary data, fusion frame, holographic representation, mean

squared error estimation, stochastic data, Wiener Filter.


IC-MaGeStiC 2021

11

Electromagnetic Flow Tomography

Marko Vauhkonen

University of Eastern Finlan, Finland

Email : [email protected]

ABSTRACT

In many fields of process industry, it is essential to be able to accurately measure the

volumetric flow rates and mass flows of different materials flowing in the process pipes.

To estimate the volumetric flow rate of a certain phase, the volumetric fraction and the

flow velocity field of the phase in a cross-section of the process pipe need to be known.

For velocity field metering, electromagnetic flow tomography (EMFT) techniques have

recently been developed in our research group.

This lecture gives an overview on the physical and mathematical basics of the EMFT

technique. Common measurement procedures and image reconstruction methods are

reviewed and some latest results of the technology to measure single and two-phase

flows are shown.

mailto:[email protected]


IC-MaGeStiC 2021

12

On Ramsey Minimal Graphs

for a 3-Matching Versus a Path on Five Vertices

Kristiana Wijaya1,* Edy Tri Baskoro2, Asep Iqbal Taufik3, Denny Riama

Silaban3

1 Graph, Combinatorics, and Algebra Research Group, Department of Mathematics, FMIPA, Universitas

Jember 2Combinatorial Mathematics Research Group, FMIPA, Institut Teknologi Bandung, Indonesia 3Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia,

Depok 16424, *Corresponding author. Email: [email protected]

ABSTRACT

Let 𝐺, 𝐻, and 𝐹 be simple graphs. The notation 𝐹 ⟶ (𝐺, 𝐻) means that any red-blue

coloring of all edges of 𝐹 contains a red copy of 𝐺 or a blue copy of 𝐻. The graph 𝐹

satisfying this property is called a Ramsey (𝐺, 𝐻)-graph. A Ramsey (𝐺, 𝐻)-graph is

called minimal if for each edge 𝑒 ∈ 𝐸(𝐹), there exists a red-blue coloring of 𝐹 − 𝑒 such

that 𝐹 − 𝑒 contains neither a red copy of 𝐺 nor a blue copy of 𝐻. In this paper, we

construct some Ramsey (3𝐾2, 𝑃5)-minimal graphs by subdivision (5 times) of one cycle

edge of a Ramsey (2𝐾2, 𝑃5)-minimal graph. Next, we also prove that for any integer

𝑚 ≥ 3, the set 𝑅(𝑚𝐾2, 𝑃5) contains no connected graphs with circumference 3.

Keywords: Ramsey minimal graph, 3-matching, path.



IC-MaGeStiC 2021

13

Two Dimensional Dynamics Simulation of

Depositing Granular Materials

Mohamad Hasan

Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas

Jember

Email: [email protected]

ABSTRACT

During deposition process, many factors play a role in the dynamics of the system

including materials’ characteristics and media onto which the materials dropped. The

stick-slip model has been applied to simulate the depositions of polydisperse granular

materials. As the size of the materials varied, and hence the criteria for colliding

materials, the dynamics and the structures of the resulting systems could be different.

The aims of this research are to investigate the dynamics of the deposition of

polydisperse materials and the structures of the resulting piles. The results show that

during the deposition process, internal landslide plays an important role, but surface

avalanche is not the main mechanism as observed in monodisperse materials. In

addition, the pile structures are not close packed and the force networks are not

dominated by diamond shapes.

Keywords: polydisperse, granular dynamics, surface avalanche, landslide, force

network.


IC-MaGeStiC 2021

14

Polynomial Rings in Post-quantum

Cryptography System

Indah Emilia Wijayanti

Universitas Gadjah Mada, Indonesia

Email : [email protected]

ABSTRACT

NTRU is one of cryptosystems which has efficient public keys. Lattice L is a set of

vectors in Rn which is generated by linearly independence vectors with linear

combination of integer coefficients. We show the roles of abstract algebra, i.e.

polynomial rings and group rings in NTRU system.


IC-MaGeStiC 2021

15

Statistical Modelling of High Zero and Heavily

Right Skewed Continuous Responses Using

GAMLSS Case Study: SINTA Score of The University of Jember 2019-

2020

I Made Tirta1,* Mohamat Fatekurohman2 Khairul Anam3

1,2,3 The University of Jember *Corresponding author. Email: [email protected]

ABSTRACT

Statistical models are frequently applied to explained the relationship between response

variable and several predictors. Statistical models are preferred to predictive models

when the focus is on the functional relationship that can be used to optimize the

response, rather than the prediction of the current situations. One of the most flexible

statistical models is GAMLSS by Stasinopoulus and Rigby, where we can choose

variety of different distributions and at the same time model the mean, and other

parameters linearly or non-linearly. We focus on continuous and high zero and right

skewed response. For this kind of response, there are several candidates of distributions

such as Zero Adjusted Gamma (ZAGA), Zero Adjusted Inverse Gamma (ZAIG) and

other mixture of continuous positive distribution, such as Gamma (ZadjGA),

Exponential (ZadjEXP), Generalized Gamma (ZadjGG) and Weibull (ZadjWEI), with

adjusted or extended definition at 0. We also build Shiny Web-GUI to enable both

menu-based input, for relatively simple models and script-based input, for more

complex models for the GAMLSS, so that non statistician researchers can apply

complex GAMLSS more easily. We apply the model to university publication data on

period 2019-2020, to model the relationship of SINTA.Score with other available

indicators. We find that the models are more related to type of distributions and their

regression parameters (continuous with heavy right tail), rather than non-linearity in the

relationship of the predictor variables. The best model for SINTA.Score is found with

Zero Adjusted Generalized Gamma (ZadjGG) distribution and for SINTA.Score.3yr is

found with Zero Adjusted Weibull distributions (ZadjWEI) with appropriate predictors.

.

Keywords: GAMLSS, Statistical model, mixed distributions R-Shiny, Web-GUI, SINTA

Score, highly skewed, zero inflated, zero adjusted


IC-MaGeStiC 2021

16

Why is it so hard to get them talking?

Dr. Natanael Karjanto

Sungkyunkwan University


ABSTRACT

This presentation discusses an effort to encourage student-instructor interactive

engagement through active learning activities in mathematics classes. We foster it via a

computer algebra system wxMaxima and student journal. We not only encourage our

students in embracing technology but also to speak out and record their active

participation during face-to-face learning. Students' feedback on teaching evaluation at

the end of the semester reveals that many dislike using the software and are against the

idea of active participation as well as recording it in a journal. We will discuss the

reason behind this resistance and provide some potential remedies to alleviate the

situation.


IC-MaGeStiC 2021

17

Mathematical Thinking In Mathematics

Learning And Research Related To

Mathematical Thinking

Erry Hidayanto

Universitas Negeri Malang


ABSTRACT

Thinking is a mental activity that involves brain work. Thinking is a rearrangement or

cognitive manipulation of both information from the environment and symbols stored in

long-term memory. Thinking in learning mathematics is called mathematical thinking.

Thinking mathematically is not the same as doing math tasks in school lessons. This is

because in school mathematics lessons usually focus on procedural steps that must be

taken to solve problems or solve problems, both problems in mathematics and problems

that arise from everyday life. In learning mathematics there are two focuses in

discussing mathematical thinking, namely focusing on the thinking process and

focusing on developing a concept. Along with the demands of 21st century learning that

students must master 4 learning skills (known as 4Cs), namely creative, critical,

collaborative, and communicative, mathematics education research has also begun to

busy researching this matter. The research in question is about how students think

critically, how students are creative, how students collaborate, and also how students

communicate their ideas. This is done because thinking it physically cannot be seen.

Research topics related to mathematical thinking include: thinking transition, thinking

transformation, creative thinking, critical thinking, mathematical connection, reflective

thinking, etc.

Keywords: thinking, mathematical, 4C.


IC-MaGeStiC 2021

18

PRESENTER OF PARALLEL

SESSION ABSTRACTS


IC-MaGeStiC 2021

19

Pattern Recognition Of Batik Madura Using

Backpropagation Algorithm

Abduh Riski1,* Ega Bandawa Winata1, Ahmad Kamsyakawuni1

1 Mathematics Department, Faculty of Mathematics and Sciences, University of Jember,

Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Since October 2, 2009, UNESCO has acknowledged batik as one of Indonesia's

intellectual properties. Throughout the archipelago, distinct and diverse batik motifs

have emerged and been produced with the passage of time; Madura batik is one of them.

The Backpropagation Algorithm is used to recognize Madura Batik Patterns in this

research. Bunga Satompok, Manuk Poter, Pecah Beling, Rumput Laut, and Sekar Jagat

are the motifs used in this study. To begin, resize the image to 200 × 200 pixels and

convert it to a grayscale image. The Gray Level Co-occurrence Matrix (GLCM)

approach is used to extract image features, and the Backpropagation Algorithm is used

to recognize them. With GLCM, the angle orientations utilized in the feature extraction

process are 0, 45, 90, and 135 degrees. There are 1, 3, and 5 hidden layers used

throughout the training process, with hidden neurons in each layer of 8, 16, and 32. The

highest accuracy is achieved when five hidden layers with 32 hidden neurons and one

hidden layer with 32 hidden neurons in each layer are used in the testing process, which

is 98 percent.

Keywords: Batik, Backpropagation, Gray Level Co-occurrence Matrix, Neural

Network.


IC-MaGeStiC 2021

20

A Modification of ECDSA to Avoid the Rho

Method Attack

Amira Zahra*, Kiki Ariyanti Sugeng

Department of Mathematics,

Faculty of Mathematics and Natural Sciences

Universitas Indonesia, Depok 16424, Indonesia * Email: [email protected]

ABSTRACT

Elliptic Curve Digital Signature Algorithm (ECDSA) is a digital signature algorithm

which utilizes elliptic curve. ECDSA consists three steps, which are key generation,

signature generation, and verification algorithm. ECDSA is used on Bitcoin transaction

to generate the users’ public keys, private keys, and signatures, and also to verify a

Bitcoin users’ signatures. There are some researches on ECDSA weak randomness

which can be exploited by attackers to reveal users’ private key, and causes thefts of the

users’ money. ECDSA weak randomness is generating a random number which is not

cryptographically secure. Some modifications of ECDSA to overcome this problem has

been done, such as generating the digital signature by using two private keys. Although

those modified algorithms overcome ECDSA weak randomness exploitation, it does not

resistant to the Rho method attack which can solve elliptic curve discrete logarithm

problem (ECDLP). In case ECDLP can be solved, users’ private key can be revealed.

Therefore, in this paper, we modify an ECDSA algorithm which overcomes the

exploitation of ECDSA weak randomness and also resistant to Rho method attack by

using three private keys.

Keywords: ECDLP, ECDSA, ECDSA weak randomness, Rho method attack.


IC-MaGeStiC 2021

21

DOPE: MDC-2 Scheme Based on PRESENT

Algorithm

*Anjeli Lutfiani and Bety Hayat Susanti

Politeknik Siber dan Sandi Negara

Jalan Haji Usa Raya, Ciseeng, Bogor, Indonesia, 16120

Email: [email protected] ; [email protected] *Corresponding author.

ABSTRACT

Modification Detection Code (MDC) as an unkeyed hash function is designed to

provide data integrity. Manipulation Detection Codes (MDC-2) is one of double-length

(2n-bit) hash-values that requiring 2 block cipher operations per block of hash input

where the output size of the hash function is twice the size of the block cipher.

Constructing hash function from block ciphers as in MDC-2 is expected to produce a

hashing algorithm that has the same efficiency and properties that are following its use

as a block cipher. In this paper, we construct a Double-length Matyas-Meyer-Oseas

based PRESENT (DOPE) hash algorithm, that implements PRESENT as a lightweight

block cipher on the MDC-2 scheme. PRESENT is used as the primary compression

function with an input 64-bit block message and 80-bit key. To analyze the performance

and resistance of DOPE against collision, a test is conducted using Yuval's Birthday

Attack. It generates minor modification input of 232 on extreme input pairs with uniform

values and input pairs with random values, and it is proven to be collision resistant.

Keywords: Hash function, lightweight block cipher, PRESENT, MDC-2, Yuval’s

Birthday Attack.




IC-MaGeStiC 2021

22

Modification Interior Point Method For Solving

Interval Linear Programming

Agustina Pradjaningsih1,*, Fatmawati2, Herry Suprajitno2

*Corresponding author. Email: [email protected]

ABSTRACT

Linear programming is mathematical programming developed to deal with optimization

problems involving linear equations in the objective and constraint functions. One of the

basic assumptions in linear programming problems is the certainty assumption.

Assumption of certainty shows that all coefficients variable or decision variables in the

model are constants that are known with certainty. However, in real situations or

problems, there may be uncertain coefficients or decision variables. Based on the

concept and theory of interval analysis, this uncertainty problem is anticipated by

making approximate values in intervals to develop linear interval programming. The

development of interval linear programming starts from linear programming with

interval-shaped coefficients, both in the coefficient of the objective function and the

coefficient of the constraint function. It was subsequently developed into linear

programming with coefficients and decision variables in intervals, commonly known as

interval linear programming. Until now, the completion of interval linear programming

is based on the calculation of the interval limit. The initial procedure for the solution is

to change the linear programming model with interval variables into two classical linear

programming models. Finally, the optimal solution in the form of intervals is obtained

by constructing two models. This paper provides an alternative solution to directly solve

the linear interval programming problem without building it into two models. The

solution is done using the interval arithmetic approach, while the method used is the

modified interior point method.

Keywords: interval linear programming, interior point method, interval arithmetic.



IC-MaGeStiC 2021

23

Classification the Melon Rinds Using

Convolutional Neural Network

Fauzan Masykur

ABSTRACT

The first signal that a melon is getting ripe is the colour that the rind changes.

Unfortunately, it is not the best indicator since the colour is not significantly different

between the ripe ones and those that are not. This article verifies the classification

between 2 types of melons, young melons and ripe melons, using the Convolutional

Neural Network (CNN) method with a dataset of 500 images of the fruits. The dataset is

classified into 2 parts, training data and testing data. While the classification method

using 2 groups of datasets have been prepared results the accuracy value 99%, the latest

melon image dataset input produces an accuracy of 52%. The difference of

classification accuracy is 47% since the images are taken at different times and lighting

conditions. Therefore, it produces different images and results in different accuracy

values.

Keywords: Convolutional Neural Network, Classification of the rind of fruits, Melon


IC-MaGeStiC 2021

24

Image Authentication Using Magic Square

Maulidyah Lailatun Najah1, Kiswara Agung Santoso2,*

1,2 Departement of Mathematic and Science, University of Jember, Indonesia


ABSTRACT

Image is a digital media that is very important to maintain its authenticity, because

images are easy to change. These changes can be influenced by 2 factors, namely

unintentional changes (eg, unstable internet in the delivery process) and intentional

changes (eg, manipulated images for certain purposes). Thus, we need a tool to

determine the authenticity of the image. The purpose o this research is image

authentication using steganography technique with magic square key. Each pixels in the

image will be formed square blocks according to the magic square size that has been

determined. The image area that has undergone changes will be detected if the pixel

value doesn’t statisfy the magic square rule.

Keywords: Authentication, Image, Magic Square



IC-MaGeStiC 2021

25

Solving Fully Fuzzy Linear Equations System

Using Metaheuristic Algorithm

Merysa Puspita Sari1*, Agustina Pradjaningsih2, Firdaus Ubaidillah3

1,2,3Mathematics Department, Faculty of Mathematics and Science, Jember University


ABSTRACT

A linear equation is an equation that is expressed in terms of a finite variable and can be

described as a straight line in the Cartesian coordinate system. A Linear equations

system is a collection of several linear equations. A Linear equations system in which

the coefficients, variables, and constants are fuzzy numbers is called a fully fuzzy linear

equations system. This study aims to apply a metaheuristic algorithm to solve a system

of fully fuzzy linear equations. While the objective function used is the minimization

objective function. The metaheuristic algorithms used in this research are Particle

Swarm Optimization (PSO), Firefly Algorithm (FA), and Cuckoo Search (CS). The

input in this research is a fully fuzzy linear equation system and parameters of the PSO,

FA, and CS algorithms. The resulting output is in the form of the best objective function

value and a convergence graph. The output is compared its accuracy with the Gauss-

Jordan elimination method from previous studies. The results obtained indicate that the

Particle Swarm Optimization (PSO) algorithm is better at solving fully fuzzy linear

equation systems than the Firefly Algorithm (FA) and Cuckoo Search (CS). This case,

seen from the value of the resulting objective function close to the value of the Gauss-

Jordan elimination method.

Keywords: Fully Fuzzy Linear Equation System, Particle Swarm Optimization, Firefly

Algorithm, Cuckoo Search, Gauss-Jordan Elimination Method



IC-MaGeStiC 2021

26

An Aplication Of Hybrid Cat-Particle Swarm

Optimization Algorithm Modified Bounded

Knapsack Problem With Multiple Constrains

1K A Santoso, 2M B Kurniawan, 3A Kamsyakawuni, 4A Riski 1-4Mathematics Department, University of Jember, Jember, Indonesia

E-mail: [email protected]

ABSTRACT

Optimization problems have become interested problem to discuss, included knapsack

problem. There are many types and variations of knapsack problems. In this paper,

authors solve modified bounded knapsack problem with multiple constraints (MBKP-

MC) using a new hybrid metaheuristic algorithm. Authors combine two popular

metaheuristic algorithms, Particle Swarm Optimization (PSO) and Cat Swarm

Optimization (CSO). The algorithm is named as Hybrid Cat-Particle Swarm

Optimization (HCPSO). The results of implementation of the algorithm are compared

with PSO and CSO algorithms. Based on the experimental results, it is known that the

HCPSO algorithm is suitable and can reach to good-quality solution within a reasonable

computation time. In addition, the new proposed algorithm performs etter than the PSO

and CSO on all MBKP-MC data used.

Keywords: Hybrid cat-particle swarm optimization, metaheuristic, modified bounded

knapsack problem


IC-MaGeStiC 2021

27

Implementation Of Hill Cipher Invers Matrix

And Cryptography In Primary Key Registration

Process On A New Student Admission Site

Mandala High School Of Economic Sciences

Muhamat Abdul Rohim1,*

1 University Of Jember *Email: [email protected]

ABSTRACT

The condition of the world that is experiencing the COVID-19 pandemic as it is today

has resulted in some activities in daily life being limited by health protocols. The

Indonesian government's policy in the academic field has forced STIE Mandala Jember,

as one of the private universities (PTS) to implement online-based new student

admissions. The identity of the registrant is very important to keep confidential during

the online-based new student registration process, so an encryption process is needed in

the running system. Hill Cipher is a cryptographic algorithm that utilizes multiplication

and inverse matrix operations. The level of complexity of the matrix operations in this

algorithm is very dependent on the order of the key matrix used, to simplify the process,

a matrix that has the order of 2x2 is used so that the key formation, encryption, and

decryption processes can be implemented in the PHP programming language and the

Laravel Framework. The results of the implementation show that PHP is not suitable for

matrix operations, so it is recommended in further research to use other programming

languages that are more suitable for matrix operations, such as python, Matlab, R, and

so on.

Keywords: Invers Matrix, Kriptografi Hill Cipher, pmb.stie-mandala.ac.id.



IC-MaGeStiC 2021

28

Learning Materials Development of Parametric

Curves and Surfaces for Modeling the Objects

Using Maple on Differential Geometry Courses

Kusno1,* Abduh Riski1

1Mathematics Department, Faculty of Mathematics and Sciences, University of Jember,


ABSTRACT

Modeling industrial objects needs the curves and surfaces formula to construct a precise

shape of a real object and simulate some process of the form creations. For the reason,

the equations study of curves and surfaces for objects modeling are essential for

resulting a requaired shape and feature of the goods. This study aims to enhance the

instructional materials of differential geometry for forth-semester college students. The

learning materials provide the students to be able to design an real object using some

parametric formulas of curves and surfaces with the software Maple. Method of

research is as follows. (a) Instructional materials design for constructing objects; (b)

Formulations and evaluations of graphs for objects modeling; (c) Modeling and

simulating to realize the objects. The research found some instructional materials and

parametric formulas of curves and surfaces to equip students to design and evaluate the

real objects and cottage industry goods. The use of Maple can help them to present the

graphs and the simulation process. The contributions of the study support the students to

learn autonomously and creatively with their knowledge, technological skills, and their

experiences in implementing some differential geometry formulas (especially the curves

and surfaces) for designing objects using tool Maple.

Keywords: Development, learning materials, curves and surfaces, modeling, parametric

formulas, Maple



IC-MaGeStiC 2021

29

Ramsey Graphs for A Star On Three Vertices

versus A Cycle

Maya Nabila, Edy Tri Baskoro, Hilda Assiyatun

Combinatorial Mathematics Research Group


Institut Teknologi Bandung, Indonesia

Emails: [email protected], [email protected], [email protected]

ABSTRACT

Let P, G, and H be simple graphs. The notation P → (G,H) means that for any red-blue

coloring of the edges of P there is a red copy of G or a blue copy of H in P. A graph P is

a Ramsey graph for a pair of (G,H) if 𝑃 → (𝐺, 𝐻). Additionally, if the graph P also

satisfies that 𝑃 − 𝑒 ↛ (𝐺, 𝐻), for any 𝑒 ∈ 𝐸(𝑃), then P is called a Ramsey (G,H)–

minimal graph. The set of all Ramsey (𝐺, 𝐻)-minimal graphs is denoted by ℛ(𝐺, 𝐻). In

this paper, we study on the Ramsey (𝐶𝑛 , 𝐾1,2)- minimal graphs. Specifically, we

construct Ramsey (𝐶𝑛 , 𝐾1,2)-minimal graphs for 𝑛 ∈ [7,10]. We also construct Ramsey

(𝐶𝑛 , 𝐾1,2) graphs by modifying the Harary graph, for any 𝑛 ≥ 6.

Keywords: Ramsey minimal graph, cycle, star





IC-MaGeStiC 2021

30

L(2,1)-Labeling of Lollipop and Pendulum

Graphs

Kusbudiono1*, Irham Af'idatul Umam1 , Ikhsanul Halikin1, Mohamat

Fatekurohman1

1 Jurusan Matematika FMIPA Universitas Jember *Corresponding author. Email: [email protected]

ABSTRACT

One of the topics in graph labeling is L(2,1) labeling which is an extension of graph

labeling. Definition of Labeling L(2,1) is a function that maps the set of vertices in the

graph to non-negative integers such that every two vertices u,v that have a distance of

one must have a label with a difference of at least two. Furthermore, every two vertices

u,v that have a distance of two must each have a label with a difference of at least one.

This study discusses the labeling of L(2,1) on a lollipop graph 𝐿𝑚,𝑛 With 𝑚 ≥ 3 and n

positive integers. The purpose of this study is to determine the minimum span value

from the labeling L(2,1) on the lollipop graph 𝐿𝑚,𝑛 and we can symbolize 𝜆2,1(𝐿𝑚,𝑛)

and to determine the minimum span value from the labeling L(2,1) on the pendulum

graph. In addition, it also builds a simulation program for labeling L(2,1) lollipop

graphs up to tremendous values of m and n. This study obtains the minimum span value

from labeling L(2,1) on a lollipop graph 𝐿𝑚,𝑛 is 𝜆2,1(𝐿𝑚,𝑛) = 2𝑚 − 2, and the

minimum span value from labeling L(2,1) of a pendulum graph Let 𝑃𝑛𝑘 with 𝑘 ≥ 4 and

𝑛 ≥ 5, is 𝑘 + 1

Keywords: Labeling L(2,1), Lollipop graph, Pendulum graph.



IC-MaGeStiC 2021

31

Gerschgorin Disc Theorem and Its Application

Alfi Y. Zakiyyah

1 Mathematics and Statistics, School of Computer Science, Bina Nusantara

University,Jakarta, Indonesia 11480 *Corresponding author. Email: [email protected]

ABSTRACT

The eigenvalues have several application for an example in the design of the car stereo

systems, where it helps to reproduce the vibration of the car due to the music. These

research survey about Gerschgorin Disc Theorem to estimate the eigenvalue. There are

several methods available for estimating the eigenvalues of a matrix geometrically. The

Cassini oval method provides an estimate of the eigenvalues in an ellipse. In addition,

the Gersgorin disc method provides an overview of the estimated value of eigens are in

a circle. Gerschgorin Disc Theorem provides an overview of the estimated eigenvalues

of the matrix. This theorem states that the eigenvalues (real or complex) of the matrix A

lies within the collection of Gersgorin circles on the complex plane.

Keywords: Eigenvalues, Cassini, Disc, Gersgorin


IC-MaGeStiC 2021

32

Modular Irregularity Strength of Generalized

Dodecahedral Graphs

I Putu Putra Gemilang Adi Guna1*, Kiki A. Sugeng2

1,2Universitas Indonesia *Corresponding author. E-mail: [email protected]

ABSTRACT

Let be a graph of order , with is an integer. Notation represents a set of vertices and

represents a set of edges. A labeling , with integer , is called modular irregular labelling

of the graph if there exist a bijective function defined by mod for every adjacent to ,

such that the weight is different for every . The minimal for which the graph admits a

modular irregular labelling is called modular irregularity strength of the graph .

Generalized Dodecahedral Graph is a graph that is built from dodecahedral graph by

adding 2 additional edges on each of the inner vertices and then we generalized the

number of vertices to with is the number of outer cycle vertices. The graph has inner

cycle vertices and outer cycle vertices, with The number of vertices of generalized

dodecahedral graph is and the number of edges is In this research, we construct a

modular irregular labelling for generalized dodecahedral graph with an upper bound of

modular irregularity strength. Moreover, we also give the lower bound of the modular

irregularity strength of

Keywords: Generalized dodecahedral graph, modular irregular labeling, modular

irregularity strength



IC-MaGeStiC 2021

33

Labelling Friendship and Windmill Graphs with

a Condition at Distance Two

Ikhsanul Halikin1, Hafif Komarullah2

1,2 Graph, Combinatorics, and Algebra Research Group, Department of Mathematics,

FMIPA, University of Jember 1Corresponding author. Email: [email protected]

ABSTRACT

A graph labelling with a condition at distance two was first introduced by Griggs and

Robert. This labelling is also known as L(2,1)-labelling. Let G=(V,E) be a non-multiple

graph, undirected, and connected. An L(2,1)-labelling on a graph is defined as a

mapping from the vertex set V(G) to the set of nonnegative integer such that for 𝑥, 𝑦 ∈𝑉(𝐺), |𝑓(𝑥) − 𝑓(𝑦)| ≥ 2 if 𝑑(𝑥, 𝑦) = 1 and |𝑓(𝑥) − 𝑓(𝑦)| ≥ 1 if 𝑑(𝑥, 𝑦) = 2, where

𝑑(𝑥, 𝑦) denoted the distance between vertex x and y. The largest number of the vertex

labels is called as span of L(2.1)-labelling. The span of a graph 𝐺 can be more than one,

the minimum value of the span of a graph 𝐺 is notated by 𝜆(2,1)(𝐺). In this paper, we

consider a graph labelling with distance two on friendship and windmill graphs.

Keywords: L(2,1)-labelling, labelling graph with distance two, minimum of span,

friendship and windmill graph



IC-MaGeStiC 2021

34

Spectrum of Unicyclic Graph

Budi Rahadjeng, Dwi Nur Yunianti, Raden Sulaiman, Agung Lukito

Department of Mathematics, Faculty of Mathematics and Natural Sciences,,

Surabaya State University


ABSTRACT

Let G be a simple graph with n vertices and let A(G) be the (0, 1)-adjacency matrix of

G. The characteristic polynomial of the graph G with respect to the adjacency matrix A

(G), denoted by 𝑃𝐺(λ) is a determinant of (λI − A(G)), where I is the identity matrix.

Suppose that 𝜆1 ≥ 𝜆2 ≥ ⋯ ≥ 𝜆𝑛 are the adjacency eigenvalues of the graph G. The

spectrum of the graph G, denoted by Spec(G), is the multiset of its adjacency

eigenvalues. Unicyclic graph is connected graph containing exactly one cycle. In this

paper we determine the spectrum of unicyclic graph containing cycle with length 6.

Keyword: characteristic polynomial, spectrum of the graph, unicyclic graph


IC-MaGeStiC 2021

35

Further Result of 𝑯-Supermagic Labeling for

Comb Product of Graphs

Ganesha Lapenangga P.1* Aryanto2 Meksianis Z. Ndii3

1, 2, 3 University of Nusa Cendana *Email: [email protected]

ABSTRACT

Let 𝐺 = (𝑉, 𝐸) and 𝐻 = (𝑉′, 𝐸′) be a connected graph. 𝐻-magic labeling of graph 𝐺 is

a bijective function 𝑓: 𝑉(𝐺) ∪ 𝐸(𝐺) → {1, 2, … , |𝑉(𝐺)| + |𝐸(𝐺)|} such that for every

subgraph 𝐻′of 𝐺 isomorphic to 𝐻, ∑ 𝑓(𝑣)𝑣∈𝑉(𝐻′) + ∑ 𝑓(𝑒)𝑒∈𝐸(𝐻′) = 𝑘. Moreover, it is

𝐻-supermagic labeling if 𝑓(𝑉) = {1, 2, … , |𝑉|}. A graph 𝐺 having such labeling called

𝐻-supermagic graph. Next, we introduce comb product of graph. Suppose 𝐺 and 𝐻 are

two connected graph and 𝑜 is vertex in 𝐻. A comb product between 𝐺 and 𝐻, denoted

by 𝐺 ⊳𝑜 𝐻, is a graph obtained by taking a copy of graph 𝐺 and |𝑉(𝐺)| copies of graph

𝐻, then identifying the 𝑖-th copy of graph 𝐻 at vertex 𝑜 to 𝑖-th vertex of graph 𝐺. In this

paper, we construct 𝐻1 ⊳ 𝐻2-supermagic labeling of graph 𝐺 ⊳ 𝐻2 where 𝐺 is 𝐻1-

supermagic graph.

Keywords: Comb product, H-supermagic labeling, 𝐻-magic.



IC-MaGeStiC 2021

36

A Minimum Coprime Number

for Amalgamations of Wheel

Hafif Komarullah1,*, Slamin2, Kristiana Wijaya3

1,3 Graph, Combinatorics, and Algebra Research Group, Department of Mathematics, FMIPA,

Universitas Jember 2 Study Program of Informatics, Universitas Jember, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Let 𝐺 be a simple graph of order 𝑛. A coprime labeling of a graph 𝐺 is a vertex labeling

of 𝐺 with distinct positive integers from the set {1, 2, … , 𝑘} for some 𝑘 ≥ 𝑛 such that

any adjacent labels are relatively prime. The minimum value of 𝑘 for which 𝐺 has a

coprime labelling, denoted as 𝔭𝔯(𝐺), is called the minimum coprime number of 𝐺. A

coprime labeling of 𝐺 with largest label being 𝔭𝔯(𝐺) is said a minimum coprime

labeling of 𝐺. In this paper, we give the exact value of the minimum coprime number

for amalgamations of wheel 𝑊𝑛 when 𝑛 is odd positive integer.

Keywords: Amalgamation, minimum coprime labeling, minimum coprime number,

wheel.


IC-MaGeStiC 2021

37

Prime-Order Cayley Graph of Dihedral Group

Ridho Surya Perkasa, Kiki Ariyanti Sugeng

[email protected] ; [email protected]

Universitas Indonesia , Indonesia

089604163792

ABSTRACT

Let (𝐷2𝑛,°) be a dihedral group, defined by 𝐷2𝑛 = {𝑓𝑖 𝑔𝑗 | 𝑓2 = 𝑔𝑛 = 𝑒, 𝑖 = 0,1 ; 𝑗 =0,1,2, ⋯ , 𝑛 − 1}, with ° is a composition function operation, 𝑓 is a reflection through x-

axis in 𝑅2 and 𝑔 is a rotation about 2𝜋

𝑛 degree counterclockwise in 𝑅2. Prime-order

Cayley graph (𝐶𝑎𝑦𝑃(𝐺, 𝑆)) is a Cayley graph where 𝑆 is a set of elements in 𝐺 that

have prime order. The set 𝑆 is called the connecting set and affects the shape of graph

𝐶𝑎𝑦𝑃(𝐺, 𝑆) in group 𝐺. In this paper, we determine the number of prime-order Cayley

graphs can be built in the dihedral group, the chromatic number of the prime-order

Cayley graphs in the dihedral group (𝜒(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆)), the diameter of a prime order

Cayley graph in the dihedral group (𝑑𝑖𝑎𝑚(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆)) and the planarity of graph

𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆). The results of this research, when is given 𝐶𝑎𝑦𝑃(𝐷2∙𝑛 , 𝑆), are as follows:

if 𝑛 = 𝑝 where 𝑝 is a prime number and |𝑆| = 2𝑝 − 1 then 𝜒(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆)) = 2𝑝,

diam(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) = 1 and 𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) is not a planar graph. Next, given

𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) if 𝑛 = 2𝑚−1, where 𝑚 ∈ ℤ, and |𝑆| = 1 then 𝜒(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆)) = 2,

diam(𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) = ∞ and 𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) is a planar graph. Given (𝐷2𝑛 , °), if 𝑛 = 𝑝𝛼

where 𝑝 is prime number and 𝛼 ∈ ℕ, then the number of 𝐶𝑎𝑦𝑃(𝐷2𝑛 , 𝑆) over (𝐷2𝑛 , 𝑆) for

𝑝 = 2 then 2(2𝛼+1) − 1, for odd number 𝑝 then 2(𝑝𝛼+𝑝−1

2) − 1.

Keywords: Cayley graph, Prime-order, Dihedral group



IC-MaGeStiC 2021

38

Distinguishing Number of the Generalized Theta

Graph

Andi Pujo Rahadi*, Edy Tri Baskoro, Suhadi Wido Saputro

Combinatorial Mathematics Research Group

Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung


ABSTRACT

A generalized theta graph is a graph constructed from two distinct vertices by joining

them with 𝑙 (>=3) internally disjoint paths of lengths greater than one. The

distinguishing number 𝐷(𝐺) of a graph 𝐺 is the least integer 𝑑 such that 𝐺 has a vertex

labeling with 𝑑 labels that is preserved only by a trivial automorphism. The partition

dimension of a graph G is the least k such that V(G) can be k-partitioned such that the

representations of all vertices are distinct with respect to that partition. In this paper, we

establish a relation between the distinguishing number and the partition dimension of a

graph. We also determine the distinguishing number for the generalized theta graph.

Keywords: distinguishing number, partition dimension, generalized Theta graph.



IC-MaGeStiC 2021

39

Application of Gröbner Bases in Ideal

Membership Problem of Polynomial Ring

k[x1,…,xn]

Johanes Irsan

ABSTRACT

The focus of this study is about the application of Gröbner basis in solving ideal

membership problem of polynomial ring 𝑘[𝑥1, … , 𝑥𝑛]. The purpose of this study is to

explain how Gröbner bases are applied in solving ideal membership problem. The

method that is used for this research is literature review. This study shows that the

properties of Gröbner bases allow Gröbner bases to be used together with division

algorithm for multivariable polynomial in solving ideal membership problem. Gröbner

bases can be constructed by using Buchberger algorithm which transforms finite bases

of an ideal to Gröbner bases. Hence, the troubles in finding Gröbner bases for solving

ideal membership problem can be avoided.

Key words: Gröbner bases, ideal, ideal membership problem, polynomial


IC-MaGeStiC 2021

40

Edge Magic Total Labeling of (𝒏, 𝒕)-kites

Inne Singgih

University of Cincinnati


ABSTRACT

An edge magic total (EMT) labeling of a graph 𝐺 = (𝑉, 𝐸) is a bijection from the set of

vertices and edges to a set of numbers defined by 𝜆: 𝑉 ∪ 𝐸 → {1,2, … , |𝑉| + |𝐸|} with

the property that for every 𝑥𝑦 ∈ 𝐸, the weight of 𝑥𝑦 equals to a constant 𝑘, that is,

𝜆(𝑥) + 𝜆(𝑦) + 𝜆(𝑥𝑦) = 𝑘 for some integer 𝑘. This paper gives the construction of

EMT labeling for certain classes and some variations of (𝑛, 𝑡)-kites.

Keywords: magic labeling, edge magic total labeling, kites.



IC-MaGeStiC 2021

41

Rainbow Connection Number of Shackle Graphs

M. Ali Hasan1,* Risma Yulina Wulandari2 M. Salman A.N.3

1,2,3 Combinatorial Mathematics Research Group, Faculty of Mathematics and Natural Sciences, Institut

Teknologi Bandung *Corresponding author. Email: [email protected]

ABSTRACT

Let 𝐺 be a simple, finite and connected graph. For a natural number 𝑘, we define an

edge coloring 𝑐: 𝐸(𝐺) → {1,2, … , 𝑘} where two adjacent edges can be colored the

same. A 𝑢 − 𝑣 path (a path connecting two vertices 𝑢 and 𝑣 in 𝑉(𝐺)) is called a

rainbow path if no two edges of path receive the same color. If there exists a 𝑢 − 𝑣

rainbow path for any two distinct vertices in 𝑉(𝐺), then 𝐺 is called rainbow connected.

In this case, 𝑐 is called a rainbow 𝑘 −coloring. The rainbow connection number of G,

denoted by 𝑟𝑐(𝐺), is the smallest number 𝑘 such that 𝐺 has a rainbow 𝑘 −coloring. In

this paper, we obtain upper and lower bounds of rainbow connection number of shackle

graph 𝐺 for any graph 𝐺. Furthermore, we show that these bounds are sharp. Then, we

get the exact value of rainbow connection number of shackle sun graph, friendship,

cycle, complete graph with one edge removed, and fan graph with two certain spokes

removed.

Keywords: Rainbow coloring, rainbow connection, shackle graph.



IC-MaGeStiC 2021

42

Implementations of Dijkstra Algorithm for

Searching the Shortest Route of Ojek Online and

a Fuzzy Inference System for Determining the

Fare Based on Distance and Difficulty of Terrain

(Case Study: in Semarang City, Indonesia)

Vani Natali Christie Sebayang1,* Isnaini Rosyida2

Universitas Negeri Semarang, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Online motorcycle taxi is one of the easiest forms of transportation, but in hilly areas

such as Semarang City, there are some obstacles, i.e, the fare produced by online

motorcycle taxis are sometimes not in accordance with the distance and difficulty of the

terrain. The problems in this study are as follows : (1) How to find the shortest route of

Ojek Online using the Dijkstra Algorithm, (2) How are the difficulties of the terrain

along the shortest routes, (3) How to determine the fare using a fuzzy inference system

where the inputs are distance and the level of difficulty of the terrain on the

geographical map in the Semarang City area .The implementation of Dijkstra's

Algorithm is used to assist in finding the shortest path and applying the Mamdani Fuzzy

Inference System to determine the fares. The results of the study based on the data

showed that the Dijkstra Algorithm can find the shortest routes of ojek online from

UNNES (initial node) to some destinantions in Semarang City. The result of using

matlab with input distance 5,5 km and terrain difficulty 300 m produced an output fare

of Rp. 13.500. Further, the result of using matlab using different terrain difficulty of 100

m poduced an output fare of Rp. 13.100. Some routes with the same distance and

different terrain heights have different fares.

Keywords: Dijkstra Algorithm, Fuzzy Inference System, Shortest Route, Fare, Distance,

Terrain Difficulty


IC-MaGeStiC 2021

43

Local Antimagic Chromatic Number of

Firecracker Graph

Lulu Tasya Ismaya, Peter John, Denny Riama Silaban*.


Faculty of Mathematics and Natural Sciences,

Universitas Indonesia, Depok 16424, Indonesia. * Email: [email protected]

ABSTRACT

Let 𝐺(𝑉, 𝐸) be a simple graph with vertex set 𝑉 and edge set 𝐸. A vertex coloring on a

graph 𝐺 is an assignment of color to vertices of 𝐺, with one color for each vertex, such

that two adjacent vertices has different color. A bijection 𝑓: 𝐸 → {1, 2, … , |𝐸|} is local

antimagic labeling of 𝐺 if the weight of two adjacent vertex is different. The weight of

𝑢 ∈ 𝑉 is 𝑤(𝑢) = ∑ 𝑓(𝑒)𝑒∈𝐸(𝑢) , where 𝐸(𝑢) is a set of edges incident to vertex 𝑢. The

number of different weights in local antimagic labeling equals to the number of colors

in the vertex coloring of 𝐺. A minimum number of colors in local antimagic labeling of

𝐺 is called local antimagic chromatic number of 𝐺, denoted by 𝜒𝑙𝑎(𝐺). A firecracker

graph, denoted by 𝐹𝑛,𝑘, is a graph obtained from 𝑛 copy of star graph by linking exactly

one leaf from each 𝑘 −star graph, where a 𝑘 −star is a graph with 𝑘 vertices. In this

paper we give the local antimagic chromatic number of firecracker graph 𝐹𝑛,𝑘, 𝑛 ≥

2 and 𝑘 ≥ 3.

Keywords: Local antimagic labeling, local antimagic chromatic number, firecracker

graph.


IC-MaGeStiC 2021

44

On Ramsey (𝟑𝑲𝟐, 𝑷𝟒)-minimal Graphs

Asep Iqbal Taufik*), Denny Riama Silaban, Kristiana Wijaya



Universitas Indonesia, Depok 16424, Indonesia

* Email: [email protected]

ABSTRACT

Let 𝐹, 𝐺, and 𝐻 be simple graphs. The notation 𝐹 → (𝐺, 𝐻) means that any red-blue

coloring of all edges of 𝐹 will contain either a red copy of 𝐺 or a blue copy of 𝐻. The

set ℛ(𝐺, 𝐻) consists of all Ramsey (𝐺, 𝐻)-minimal graphs, namely all graphs 𝐹

satisfying 𝐹 → (𝐺, 𝐻) but for each 𝑒 ∈ 𝐸(𝐹), (𝐹 − 𝑒) ↛ (𝐺, 𝐻). Let 𝑡𝐾2 be a matching

with t edges and 𝑃𝑛 be a path on n vertices. In this paper, we construct a new

disconnected Ramsey minimal graph in ℛ(3𝐾2, 𝑃4) from graph in ℛ(2𝐾2, 𝑃4).

Furthermore, we subdivision.construction new Ramsey minimal graphs in ℛ((𝑚 +1)𝐾2, 𝑃4) from Ramsey minimal graphs in ℛ(𝑚𝐾2, 𝑃4) by subdivision operation.

Keywords: Matching, path, Ramsey minimal graphs,


IC-MaGeStiC 2021

45


𝑨𝒎𝒂𝒍(𝑺𝒎+𝟏, 𝑺𝒏+𝟏)

Amelia Nurannisa Hadi, Peter John, Denny Riama Silaban*

Faculty of Mathematics and Natural Sciences, Universitas Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Let 𝐺 = (𝑉, 𝐸) be a graph 𝐺 with 𝑛 vertices and 𝑚 edges and let 𝑓: 𝐸 → {1,2, … , 𝑚} be

a bijective function. For every vertex 𝑢 ∈ 𝑉(𝐺), the weight of vertex 𝑢 is 𝑤(𝑢) =∑ 𝑓(𝑒)𝑒∈𝐸(𝑢) , where 𝐸(𝑢) is a set of edges that are incident to vertex 𝑢. If 𝑤(𝑢) ≠ 𝑤(𝑣)

for every two adjacent vertices 𝑢, 𝑣 ∈ 𝑉(𝐺), then 𝑓 is called a local antimagic labelling

of 𝐺. Let the vertices of G be colored such that vertices with different weight have

different color. The local antimagic chromatic number of 𝐺, denoted by 𝜒𝑙𝑎(𝐺), is the

minimum number of colors needed for coloring 𝐺 induced from local antimagic

labelling of 𝐺. Let 𝑆𝑛 be a star graph with 𝑛 + 1 vertices. A graph 𝐴𝑚𝑎𝑙(𝑆𝑚+1, 𝑆𝑛+1) is

a vertex amalgamation of a leaf from star graphs 𝑆𝑚+1 and 𝑆𝑛+1. In this paper, we find

the local antimagic chromatic number of 𝐴𝑚𝑎𝑙(𝑆𝑚+1, 𝑆𝑛+1).

Keywords: Local antimagic chromatic number, local antimagic vertex coloring, vertex

amalgamation graph



IC-MaGeStiC 2021

46

Local Antimagic Vertex Coloring of Gear Graph

Masdaria Natalina Br Silitonga, Kiki Ariyanti Sugeng


Faculty of Mathematics and Sciences


Email: [email protected], [email protected]

ABSTRACT

Let 𝐺 = (𝑉, 𝐸) be a graph with vertex set 𝑉 and edge set 𝐸. The local antimagic

labeling 𝑓 of a graph 𝐺 with edge-set 𝐸 is a bijection map from 𝐸 to {1, 2, … , |𝐸|} such

that 𝑤(𝑢) ≠ 𝑤(𝑣), where 𝑤(𝑢) = ∑ 𝑓(𝑒)𝑒∈𝐸(𝑢) and 𝐸(𝑢) is the set of edges incident to

𝑢. Thus, any local antimagic labelling induces a proper vertex coloring of 𝐺 where the

vertex 𝑣 is assigned the color 𝑤(𝑣). The local antimagic chromatic number, denoted by

𝜒𝑙𝑎(𝐺), is the minimum number of colors taken over all colorings induced by local

antimagic labelings of 𝐺. In this paper, we present the local antimagic chromatic

number 𝜒𝑙𝑎(𝐺𝑛) of a gear graph. A gear graph is a graph obtained by inserting an extra

vertex between each pair of adjacent vertices on the perimeter of a wheel graph 𝑊𝑛.

Thus, 𝐺𝑛 has 2𝑛 + 1 vertices and 3𝑛 edges.

Keywords: Antimagic labeling, Local antimagic labeling, Local antimagic chromatic

number, Gear graph.



IC-MaGeStiC 2021

47

Local Antimagic Vertex Coloring of Corona

Product Graphs 𝑷𝒏 ∘ 𝑷𝒌

Setiawan, Kiki Ariyanti Sugeng


Faculty of Mathematics and Sciences


Email: [email protected], [email protected]

ABSTRACT

Let 𝐺 = (𝑉, 𝐸) be a graph with vertex set 𝑉 and edge set 𝐸. A bijection map 𝑓: 𝐸 →{1,2, … , |𝐸|} is called a local antimagic labeling if, for any two adjacent vertices u and

v, they have different vertex sums, i.e. 𝑤(𝑢) ≠ 𝑤(𝑣), where the vertex sum 𝑤(𝑢) =

𝛴𝑒 ∈𝐸(𝑢) 𝑓(𝑒), and 𝐸(𝑢) is the set of edges incident to 𝑢. Thus any local antimagic

labeling induces a proper vertex coloring of 𝐺 where the vertex 𝑣 is assigned the color

(vertex sum) 𝑤(𝑣). Let G and H be two graphs. The Corona product 𝐺 ⨀ 𝐻 is obtained

by taking one copy of G and |𝑉(𝐺)| copies of H, and by joining each vertex of the ith

copy of H to the ith vertex of G, where 1 ≤ i ≤ |𝑉(𝐺)|. The local antimagic chromatic

number, denoted 𝜒𝑙𝑎(𝐺), is the minimum number of colors taken over all colorings

induced by local antimagic labelings of 𝐺. In this paper, we present the local antimagic

chromatic number 𝜒𝑙𝑎(𝑃𝑛 ⨀ 𝑃𝑘) for the corona product of path 𝑃𝑛 and 𝑃𝑘 where k is a

small number.

Keywords: Antimagic labeling, Local antimagic labeling, Local antimagic chromatic

number, Corona product graph, Path



IC-MaGeStiC 2021

48

Rainbow (Vertex) Connection Numbers of

Bat Graphs and Covid Graphs

Suci Yefri Fadillah1,*, Maya Nabila1, A.N.M. Salman1

1 Combinatorial Mathematics Research Group,

Faculty of Mathematics and Natural Sciences,

Institut Teknologi Bandung, Indonesia *Corresponding author. Email: [email protected], [email protected],

[email protected]

ABSTRACT

Let 𝐺 = (𝑉(𝐺), 𝐸(𝐺)) be a nontrivial, finite, simple, and connected graph. For some

𝑘 ∈ ℕ, define an edge 𝑘-coloring 𝑐: 𝐸(𝐺) → {1,2, … , 𝑘}. A path 𝑃 in 𝐺 is said a rainbow

path, if there are no two edges of 𝑃 colored by a same color. A rainbow path connecting

two vertices 𝑢 and 𝑣 in 𝐺 is called a rainbow (𝑢, 𝑣)-path. A graph 𝐺 is called a rainbow-

connected under 𝑐, if for every two vertices 𝑢 and 𝑣 in 𝐺, there exists a rainbow (𝑢, 𝑣)-

path. In this case, the coloring 𝑐 is called a rainbow 𝑘-coloring of 𝐺. The rainbow

connection number of 𝐺, denoted by 𝑟𝑐(𝐺), is the minimum 𝑘 such that 𝐺 has a

rainbow 𝑘-coloring.

For some 𝑙 ∈ ℕ, define a vertex 𝑙-coloring 𝑐∗: 𝑉(𝐺) → {1,2, … , 𝑙}. A path 𝑃 in 𝐺 is

called a rainbow vertex-path, if each internal vertex of 𝑃 has a distinct color. If for two

vertices 𝑢 and 𝑣 in 𝑉(𝐺) there is a rainbow vertex path connecting them, we say that 𝐺

is a rainbow vertex connected graph under 𝑐∗. The smallest positive integer 𝑙 such that

𝐺 has a rainbow vertex 𝑙-coloring is called the rainbow vertex-connection number of 𝐺, denoted by 𝑟𝑣𝑐(𝐺).

In this paper, we intoduce two classes of graphs, namely bat graphs and covid graphs.

We determine the rainbow connection number and the rainbow vertex connection

number of these graphs.

Keywords: bat graph, covid graph, rainbow connection number, rainbow vertex

connection number




IC-MaGeStiC 2021

49

Magic and Antimagic Decomposition of

Amalgamation of Cycles

Sigit Pancahayani1,* Annisa Rahmita Soemarsono2 Dieky Adzkiya3

Musyarofah4

1 Department of Statistics, Institut Teknologi Kalimantan, Balikpapan, Indonesia 2 Department of Mathematics, Institut Teknologi Kalimantan, Balikpapan, Indonesia 3 Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya,

Indonesia 4 Department of Physics, Institut Teknologi Kalimantan, Balikpapan, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Consider 𝐺 = (𝑉, 𝐸) as a finite, simple, connected graph with vertex set 𝑉 and edge set

𝐸. 𝐺 is said to be a decomposable graph if there exists a collection of subgraphs of 𝐺,

say ℋ = {𝐻𝑖|1 ≤ 𝑖 ≤ 𝑛} such that for every 𝑖 ≠ 𝑗, 𝐻𝑖 is isomorphic to 𝐻𝑗, ⋃ 𝐻𝑖𝑛𝑖=1 = 𝐺

and should satisfy that 𝐸(𝐻𝑖) ∩ 𝐸(𝐻𝑗) = ∅ if 𝑖 ≠ 𝑗. Let 𝑓: 𝑉(𝐺) ∪ 𝐸(𝐺) →

{1,2, … , |𝑉(𝐺)| + |𝐸(𝐺)|} be a bijection mapping such that every subgraph in ℋ has

the same total of valuation 𝑤(𝐻𝑖) = ∑(𝑓(𝑣) + 𝑓(𝑒)) = 𝑘 for 𝑣 ∈ 𝑉(𝐻𝑖) and 𝑒 ∈ 𝐸(𝐺).

In this paper, we said 𝑘 as a magic constant. If every subgraph 𝐻𝑖 ≅ 𝐻 of 𝐺 admits such

labeling, then 𝐺 admits 𝐻 −magic decomposition. Otherwise, if the total values among

all subgraphs are different, then 𝐺 admits 𝐻 −antimagic decomposition. In this

research, a graph derived from amalgamating some cycles in a terminal vertex is the

object to be investigated to find its property regarding magic decomposition.

Furthermore, we find that the vertex amalgamation of some identical cycles admits both

magic and antimagic decomposition, which depends on its order.

Keywords: Amalgamation, magic, antimagic, decomposition, cycle.



IC-MaGeStiC 2021

50

On The Minimum Span of Cone, Tadpole, and

Barbell Graphs

Hafif Komarullah1, Ikhsanul Halikin2, Kiswara Agung Santoso3

1,2,3 Graph, Combinatorics, and Algebra Research Group, Department of Mathematics,

FMIPA, University of Jember 2Corresponding author. Email: [email protected]

ABSTRACT

Let 𝐺 be a simple and connected graph with 𝑝 vertices and 𝑞 edges. An 𝐿(2,1)-labelling

on the graph 𝐺 is a function 𝑓: 𝑉(𝐺) → {0, 1, … , 𝑘} such that every two vertices with

distance one receive labels that differ by at least two, and every two vertices at distance

two receive labels that differ by at least one. A number k is called as span of L(2.1)-

labelling, if k is the largest number of the vertex labels. The span of a graph 𝐺 can be

more than one, the minimum value of the span of a graph 𝐺 is notated by 𝜆(2,1)(𝐺). In

this paper, we determine the minimum span of cone, tadpole, and barbell graphs

Keywords: 𝐿(2,1) labelling, minimum of span, cone, tadpole, and barbell graphs.



IC-MaGeStiC 2021

51

Hurdle Regression Modelling on The Number of

Deaths from Chronic Filariasis Cases in

Indonesia

Nur Kamilah Sa'diyah1*,Ani Budi Astuti2*,and Maria Bernadetha T.

Mitakda2

1 Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya

University 2 Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya

University

*Corresponding author. Email: [email protected] and [email protected]

ABSTRACT

One model to explain the relationship between predictor variable and response cvariable

in the form count is Poisson Regression. An important assumption in Poisson

Regression analysis is equidispersion. In certain cases, there are many zero values in the

response variable, thus causing the variety value to be greater than the average or called

overdispersion that can be overcome with the Hurdle model. Filariasis disease caused

by filaria worm that cause swelling of the limbs in humans. There are several provinces

in Indonesia have cases of chronic filariasis death is quite high, namely West Papua

Province with a death rate of 459 people. The Hurdle regression model is appropriately

used to model the number of cases of chronic filariasis death in Indonesia because the

data contains overdispersion. This study will be compared two regression models

Hurdle, namely the Hurdle Poisson regression and regression Hurdle Negative Binomial

Regression. The results showed that the negative Binomial Hurdle regression model

was better than that of the Hurdle Poisson regression model in modeling cases of

filariasis in Indonesia with AIC value of 207.8084.

Keywords: Filariasis, MLE, Overdispersion, Hurdle Regression



IC-MaGeStiC 2021

52

Bayesian Statistical Modeling Perspective in the

Covid-19 Disaster Mitigation Series in East Java

Region

Ani Budi Astuti 1*, Ni Wayan Surya Wardhani 2, Maria Bernadetha T.

Mitakda3, and Denisa Lauvil Maulidia4

1,2,3 Statistics Department, Faculty of Mathematics and Natural Sciences, Brawijaya

University, Malang 4 Undergraduate Student of Statistics Department, Faculty of Mathematics and Natural

Sciences, Brawijaya University, Malang


ABSTRACT

Modeling is a very important tool to simplify complex problems that exist in society, so

that they are easy to understand and useful in various aspects of life. The perspective of

statistical modeling based on probability that naturally and actually provides a concept

of an element of uncertainty in the model, where this must exist and must occur in

various aspects of life, so that this concept brings a model that is built according to

natural conditions in the data. The advantage of Bayesian statistical modeling is that it

maintains the data driven concept with any form of distribution and any sample size and

works directly on the original data. Various disaster mitigation efforts for the Covid-19

disease in Indonesia have been carried out as a series of efforts for supervision,

monitoring, control, and prevention of the Covid-19 disease which is very dangerous for

human safety. Through proper Bayesian statistical modeling, it will be able to provide

accurate predictions and forecasts, so that information from the model can be used as a

reference for carrying out various disaster mitigation actions. The purpose of this study

is to build an appropriate statistical model for the addition of Covid-19 cases per day in

East Java by modeling the Bayesian Model Averaging (BMA) Markov Chain Monte

Carlo (MCMC) at ARIMA. The results showed that the statistical model with the

Bayesian approach that was built was able to properly follow the original data pattern

with the ARIMA BMA-MCMC calibration model consisting of the ensemble model

components ARIMA(0,1,3) ARCH(1), ARIMA(0,1,3 ) ARCH(2), and ARIMA(0,1,3)

GARCH(1,1) with the validity value of the Root Mean Square Error model is 590.1058.

Keywords: ARIMA, Bayesian Model Averaging, Covid-19, MCMC, Statistical

Modeling Perspective.



IC-MaGeStiC 2021

53

High Order Three-Steps Newton Raphson-like

schemes for Solving Nonlinear Equation Systems

Rizki Multazamil Fatahillah1, M Ziaul Arif*1, Rusli Hidayat1,

Kusbudiono1, Ikhsanul Halikin1

1 Department of Mathematics, FMIPA, University of Jember *Corresponding author. Email: [email protected]

ABSTRACT

This study proposes several new 3-steps schemes based on the Newton-Raphson

method for solving non-linear equation systems. The proposed schemes are analysed

and formulated based on the Newton-Raphson method and the Newton-cotes open form

numerical integration method. In general, the schemes can be considered as a predictor

and corrector principles. In the first and the third steps, the Newton-Raphson method is

applied. Furthermore, Newton-cotes Open Form numerical integration modification is

operated in the second step of the proposed schemes. The convergence analysis of the

proposed schemes is given. It shows that the proposed scheme provides the 8th order of

convergence. The performance of the proposed schemes is compared and assessed with

several numerical examples.

Keywords: Nonlinear Equation Systems, Newton-Raphson Method, Newton-Cotes Open

Form.



IC-MaGeStiC 2021

54

Root Water Uptake Process for Different Types

of Soil in Unsteady Infiltration from Periodic

Trapezoidal Channels

Millatuz Zahroh1,*Imam Solekhudin2

1 Mathematics Departement, Universitas Jember 2Mathematics Departement, Universitas Gadjah Mada *Corresponding author. Email: [email protected]

ABSTRACT

This study involved a non-linear partial differential equation known as Richard’s

Equation. An unsteady infiltration from trapezoidal periodic irrigation channel with

root-water uptake are considered as the problem. To solve the problem, A set of

transformations, kirchhoff, dimensionless variables, Batu’s and Laplace transformation,

are employed to transform Richard’s Equation into a modified Helmholtz equation.

Finally, The transformation is solved numerically using Dual Reciprocity Method

(DRM) with predictor-corrector scheme. Employing Gaver-Stehfest formulae and

diffusivity factor, distributions of root water uptake process are obtained as sink term of

the problem.

Keywords:. root water uptake, unsteady infiltration, DRM, diffusivity factor



IC-MaGeStiC 2021

55

Analysis of Factors Affecting Poverty Depth

Index in Papua Province Using Panel Data

Regression

Rufina Indriani1,* Erma Oktania Permatasari2

1 Statistics 2 Statistics *Corresponding author. Email: [email protected],

[email protected]

ABSTRACT

One of the main problems in Papua Province is poverty, this can be proven from the

poverty indicators in Papua Province are greater than other provinces. One of the

measure of Poverty is the Poverty Depth Index (P1), which is a measure of the regional

poverty gap. The value measured from the average expenditure gap of the poor against

the poverty line. The Poverty Depth Index value in Papua Province in 2019 was 7.17,

which is very different from the Poverty Depth Index in Indonesia which was only 1.55.

This study will analyze the factors that affect the Poverty Depth Index in Papua

Province using the Poverty Depth Index (P1) data as response variable and predictor

variables are Human Development Index, Life Expectancy, average expenditure per

capita in one month, Literacy Rate Age 15 years and over, and the percentage of

households that have purchased Poor Rice/Prosperous Rice in 2012 to 2019 using the

panel data regression method. Panel data regression is used because this method can

combine cross section data with time series data. The results of the regression analysis

show that the best model is Fixed Effect Model (FEM) with cross-section weighted. The

model has an R-square value of 82.5% with significant variables are Human

Development Index and average expenditure per capita in one month.

Keywords: Fixed Effect Model (FEM), Poverty Depth Index, Panel Data Regression



IC-MaGeStiC 2021

56

A Mathematical Model for COVID-19 to Predict

Daily Cases using Time Series Auto Regressive

Integrated Moving Average (ARIMA) Model in

Delhi Region, India

Tarunima Agarwal1, Stavelin Abhinandithe K2

1Modern School, Barakhamba Road, New Delhi, India,

Email id: [email protected] 2Assistant Professor, Division of Medical Statistics, Faculty of Life Sciences, JSSAHER, Mysuru,

Karnataka, India

Email id: [email protected]

ABSTRACT

Coronavirus disease (COVID-19) is an infectious disease caused by a coronavirus

which is widely spreading throughout the world. Various countries have adopted

different strategies to control the spread of the disease. Many studies have adopted the

mathematical modeling to predict the cases during the pandemic. In our study we have

used Box- Jenskin’s time series Auto Regressive Integrated Moving Average (ARIMA)

mathematical model. MATERIALS AND METHODS: Publicly available data of

daily COVID-19 confirmed cases along with Meteorological variables were considered

using Expert Modeler in SPSS to Predict and forecast COVID-19 cases in Delhi region,

India. RESULTS: Spearman’s correlation was used to find the relationship between

COVID-19 cases along with Meteorological variables. Humidity, rainy days and

Average sunshine were found to be significant. ARIMA (0, 1, 14) model was found to

be best fitted model for the given data with R square value of fitted model is 0.920.

Ljung-Box test value is 39.368 with p value showing significant, indicating that the

fitted model is adequate to predict and forecast COVID-19 cases. CONCLUSION:

ARIMA (0, 1, 14) mathematical model was selected as a best suited model to predict

and forecast the incidence of COVID-19 cases in Delhi region, which would be useful

for the policymakers for better preparedness.




IC-MaGeStiC 2021

57

Symmetry Functions With Respect To Some

Point in Rn and Their Properties

Firdaus Ubaidillah

Department of Mathematics Faculty of Mathematics and Natural Sciences

University of Jember – Jember 68121 Indonesia


ABSTRACT

A function 𝑓 ∶ 𝑅 → 𝑅 is said to be an odd function if 𝑓(−𝑥) = −𝑓(𝑥) for every 𝑥 in 𝑅.

The graph of an odd function is symmetric with respect to the origin, that is the point

(0,0). The aims this paper are generalize odd functions on 𝑅𝑛 and introduce symmetry

functions with respect to some point in 𝑅𝑛. Further, this paper discusses some properties

of odd functions on 𝑅𝑛 and symmetry functions with respect to some point in 𝑅𝑛.

Keywords: odd function, symmetry function, symmetric graph.



IC-MaGeStiC 2021

58

Hanging Rotera Modeling by Joining

Deformation Result of Space Geometry Objects

Bagus Juliyanto1,* Een Ubaningrum1 Firdaus Ubaidillah

1 Mathematics Department, Faculty of Mathematics and Sciences, University of Jember,


ABSTRACT

The hanging rotera is a small lamp covered by a glass lid with a light source comes

from a burning candle or LED (Light Emitting Diode) candle and hung on a support

pole that is hooked to the rotera connector. This paper deals with the modeling of the

hanging rotera with the aim to obtain a model of the various and symmetrical

components of the hanging rotera using deformation techniques on space geometric

objects. The components of space geometric objects that used were tubes, cones, regular

hexagon prisms, torus, and spheres. This research method determines the size and

modeling the hanging rotera using the deformation technique. The deformation

techniques that applied were cutting, dilatation, roteration, reflection, revolution curves,

interpolation of line segments and curves, and Bezier curves of 2, 3, and 4 degrees. In

order to join the components of the hanging rotera, we have to notice the radius and the

distance of the center of gravity to the corner points of a regular hexagon polygon on

each rotera component. The integration of the hanging rotera components as a whole

part requires symmetry through the vertical axis which is divided into three parts,

namely the rotera part, the connecting part, and the support pole part. We obtain 125

models with five variations on each component of the hanging rotera as the result. The

hanging rotera model that we have observed can be visualized using Maple 18.

Keywords: Hanging rotera, Deformation technique, Modeling, Space geometric objects


IC-MaGeStiC 2021

59

Generalization of Chaos Game on Polygon

Kosala D. Purnomo1,*

1 Department of Mathematics, University of Jember *Corresponding author. Email: [email protected]

ABSTRACT

The original chaos game has been applied to the triangular attractor points. With the

rules for selecting attractor points randomly, the points generated in large iterations will

form like a Sierpinski triangle. Several studies have developed it on the attractor points

of quadrilaterals, pentagons, and hexagons which are convex in shape. The fractals

formed vary depending on the shape of the attractor points. This paper will study the

development of chaos game at attractor points in the form of arbitrary convex and non-

convex polygons. The results obtained are consistent with previous results. The

resulting fractal is in the form of a convex polygon built from the outermost points of its

attractor.

Keywords: Fractals, chaos game, attractor points, convex polygon.


IC-MaGeStiC 2021

60

Information Retrieval Using The Matrix Method Case Studi: Three Popular Online News Sites In Indonesia

Ferry wiranto1,* I Made Tirta2, Kiswara Agung Santoso3

1,2,3 Universitas Jember,


ABSTRACT

This research is part of data mining, a sub-section of information retrieval and text

mining. This research focuses on finding an approach in finding relevant documents

online news documents. In this case, the author uses news from 3 news sites that are

quite popular in Indonesia, namely tribunnews.com, detik.com, and liputan6.com. In the

process of searching for relevant news documents, the authors determine the threshold

value first by calculating the average similarity value of the documents used as the

experimental sample. So that the resulting threshold value is a determinant of the

similarity value of each document to be used. The author uses several techniques to

assist the research process, such as text mining with the TALA method and news

document representation techniques using the matrix method and finally using the

cosine size method to determine the similarity of documents with matrix-based search

data.

Keywords: Data mining, text mining, matrix method, cosine size, sparse matrix.


IC-MaGeStiC 2021

61

The Application of the Bayesian Framework in

The Joint Reconstruction of Conductivity and

Velocity of Two-Phase Flows Problems by Using

Dual-Modality

M. Ziaul Arif1,2*, Ossi Lehtikangas2,3, Aku Seppänen2, Ville Kolehmainen2,

and Marko Vauhkonen2.

1Department of Mathematics, FMIPA, University of Jember. Jln. Kalimantan 37, Jember 68121 2 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland 3 Silo.AI, Kuopio, Finland

*) E-mail: [email protected]

ABSTRACT

The two-phase flows, including oil-water, gas-water, or solids-water, is complex

phenomenon in the process industry. Several approaches have been proposed to

estimate velocity, phase fraction and flow rate of the flows. However, the accuracy of

the estimation is still a big problem. In this paper, a dual-modality consisting of

Electromagnetic Flow Tomography (EMFT) and Electrical Tomography (ET) imaging

which provide information on the velocity field and electrical conductivity distribution,

respectively, is considered. Furthermore, the flow rate of the fluid can be further

computed from those estimations. The paper aims to improve the accuracy of the EMFT

and ET reconstructions by using the joint reconstruction within the Bayesian inverse

problems framework with a cross-covariance matrix as an additional prior model. The

proposed approach is tested with numerical simulations, actual computational fluid

dynamics (CFD), and several different prior models. The comparison results show that

the proposed approaches with a cross-covariance model can improve the accuracy of the

estimates.

Keywords: Dual-modality imaging, Electrical tomography, Electromagnetic flow

tomography (EMFT), Inverse problems, Two-phase flows



IC-MaGeStiC 2021

62

Diabetes Mellitus Screening Model using

Fuzzy K-Nearest Neighbours in Every Class

Algorithm

Maizairul Ulfanita1, Alfian Futuhul Hadi2*, Mohamat Fatekurohman2

1) Department of Matematics, University of Jember, Jember 68121, Indonesia

2) Data Science Research Group, Departement of Matematics, University of Jember, Jember 68121,

Indonesia

*)Corresponding author. Email: [email protected]

ABSTRACT

Heart disease is the number one killer in the world. Someone who those with the

greatest potential for heart disease are people with Diabetes Mellitus (DM). DM that is

diagnosed early can prevent the sufferer from the risk of heart disease and various other

complications. This study aims to diagnosed early or DM disease screening using

machine learning methods. One of the machine learning algorithms that can be used for

data classification is Fuzzy K-Nearest Neighbours in Every Class (FKNNC). FKNNC is

a classification technique that makes predictions using number of k nearest neighbour’s

in each class of a test data. The dataset was divided into two parts along with percentage

80% data train and 20% data test. The variables used were gender, glucose levels, blood

pressure, insulin levels, body mass index, diabetes pedigree function and age as

independent variable as well as diabetes class variable as dependent variable. The

testing of FKNNC algorithm obtained confusion matrix with accuracy of 86%.

Meanwhile, the Area Under Curve (AUC) value obtained of 0,86. The FKNNC model

is the best model because it has high accuracy and the AUC value obtained shows the

classification model with good ability. Therefore, the FKNNC model can be used to

classify patients into potential groups positive DM or negative DM with high enough

accuracy so that it can help the world medical.

Keywords: diabetes mellitus, machine learning, FKNNC, classification.



IC-MaGeStiC 2021

63

Bayesian Accelerated Failure Time Model

and its Application to Preeclampsia

Dennis Alexander1,* Sarini Abdullah1

1 Department of Mathematics, Faculty of Mathematics and Natural Sciences, University

of Indonesia, Depok, 16424, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Preeclampsia (PE) often described as new-onset hypertension and proteinuria during the

third trimester of pregnancy. PE, in particular, is one of the most feared complications

of pregnancy because it can progress rapidly to serious complications, including death

of both mother and fetus. It is important to get a better understanding about the factors

that might affect the PE condition in pregnant women. Therefore, in this study, we tried

to model the relationship between several factors and the time until deliveries under the

PE condition. Data on 925 patients at gynecology department in a hospital in Jakarta

were used in the analysis. A survival regression model, Accelerated Failure Time (AFT)

model, was proposed to model the delivery time under PE condition and important

factors that influenced the time. Model parameters were estimated using Bayesian

method. The results revealed some important factors in explaining the time of deliveries

and we also produced the formulation for calculating the estimated probability of

delivery given a specific gestational time and patient’s characteristics.

Keywords: Delivery Time, Gestational Time, Survival Regression Model



IC-MaGeStiC 2021

64

Multiple Discriminant Analysis Altman Z-Score,

Multiple Discriminant Analysis Stepwise and K-

Means Cluster for Classification of Financial

Distress Status in Manufacturing Companies

Listed on The Indonesia Stock Exchange in 2019

Hazrina Ishmah1,*, Solimun2, Maria Bernadetha Theresia Mitakda3

Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya

University, Malang, 65145, Indonesia

*Corresponding Email: [email protected]

ABSTRACT

This study uses the MDA (Multiple Discriminant Analysis) Altman Z-Score to

predict the status of financial distress in manufacturing companies listed on the

Indonesia Stock Exchange in 2019. MDA Stepwise model is used to prove that the

variables used in the MDA Altman Z-Score method are the best variables for

predicting financial distress status. MDA Altman Z-Score uses five variables from

financial ratios. Variables used in Altman Z-Score are working capital/total assets,

retained earnings/total assets, earnings before interest and taxes/total assets, market

value equility/book value of total liabilities and sales/total assets. The variables used

in MDA Stepwise are 38 financial ratios and validate that the MDA Altman Z-Score

is appropriate in classifying manufacturing companies experiencing financial distress

in 2019 using the K-Means cluster. In this study, the results obtained for the best

prediction of financial distress status using MDA Stepwise seen from the highest

accuracy value (84.54%) and significant variables in predicting financial distress

status are capital market to book value of debt, sales/work capital, and sales/current

assets variables. The best classification for manufacturing companies if they are

classified into 3 groups, namely the group not experiencing financial distress, gray

area and experiencing financial distress.The category of the grouping of companies

resulted in 73 companies experiencing financial distress one company was in the

gray area and nine companies did not experience financial distress.

Keywords: Financial Distress, K-Means Cluster, MDA Altman Z-Score, MDA Stepwise.



IC-MaGeStiC 2021

65

Double Bootstrap Method for Autocorrelated

Data in Process Control

Jauharin Insiyah1,*, Suci Astutik2, Loekito Adi Soehono3

Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya

University, Malang, 65145, Indonesia

*Corresponding Email: [email protected]

ABSTRACT

Process control often induces a correlation between observations in the form of time

series or called autocorrelation. T2 Hotelling as one of the popular multivariate control

chart is no longer sensitive to small and moderate mean shifts derived from the

autocorrelation data. In this study, T2 Hotelling performance was improved by

determining the upper control limit (UCL) using Double Bootstrap based on the residual

first-order Vector Autoregressive Model (VAR). To test the performance of the

proposed method, simulation data was used starting from small shift δ = 0.05 to large

shifts δ = 3.0 with comparison of the Average Run Length (ARL) value with false

alarm probability α = 0.005. The result shows that the control limit using Double

Bootstrap method on the residual VAR Chart is more sensitive for all shifts than the

single Bootstrap and classic T2 Hotelling method. Thus, the control chart is not only

good at detecting shifts but also provide the way to minimize errors in a multivariate

process control.

Keywords: 𝑇2Hotelling Control Chart, Vector Autoregressive Models (VAR),

Double Boostrap.



IC-MaGeStiC 2021

66

Generalized Space Time Autoregressive-X

(Gstar-X) Model In Forecetting Cabbage

Production In Malang

Lely Holida1 , Atiek Iriany2, Ni Wayan S. Wardhani3

1,2,3Department of Statistics, Faculty of Mathematics and Natural Since, Brawijaya

University, Malang, Indonesia

Email: [email protected], [email protected], [email protected]

ABSTRACT

Horticultural crops are cultivated plants that are very prospective to be developed

through agribusiness, one of the horticultural commodities is cabbage. The increase in

cabbage production in Indonesia has contributed quite well to the development of

national horticultural crop production. The multivariate time series method that

combines elements of time and location (space time) dependencies is the Generalized

Space Time Autoregressive (GSTAR) model. The GSTAR model involving exogenous

variables is known as the GSTARX model. The exogenous variables used are the metric

scale (rainfall) and the non-metric scale, namely calendar variations and interventions in

the form of rising fuel prices (BBM). The case study in this study was applied to

forecasting cabbage production data in ten sub-districts in Malang, namely

Poncokusumo, Wajak, Turen, Bululawang, Pagelaran, Tajinan, Tumpang, Singosari,

Karangploso and Ngantang. The purpose of this study was to obtain a suitable

GSTARX model for forecasting data on cabbage production in ten sub-districts in

Malang. The results of GSTARX modeling for forecasting cabbage production data in

ten sub-districts in Malang are GSTARX-GLS ([1,12]). Univariate modeling by adding

exogenous variables gives a smaller RMSE value than without involving exogenous

variables. Likewise, the level of forecasting accuracy shows that the univariate model is

better than the GSTARX-GLS. This is based on the minimum outsample RMSE value.

Keywords: Generalized Space-Time Autoregressive-X (GSTARX), Cabbage, root

mean square error (RMSE), 𝑅 2 .




IC-MaGeStiC 2021

67

Contact Tracking With Social Network Analysis

Graph

Alvida Mustika Rukmi1 ,Wildan Zakky2 , M. Lutfhi Shahab3

1,2,3 Department of Mathematics, Institut Teknologi Sepuluh November Campuss ITS, Sukolilo, Surabaya,

60111, Indonesia

Email: [email protected] [email protected] [email protected]

ABSTRACT

In 2020, the world is facing a Covid-19 virus pandemic. The fields of epidemiology and

networks are needed in dealing with its spread. Individual (contact) tracing is an

important control measure in the spread of infectious diseases. The network of contacts

describes the potential pathways for the spread of the disease. To describe the

complexity of the spread of disease, the principles of network science need to be studied

and applied to the creation of a contact tracing system of persons exposed to infectious

diseases. Social Network Analysis (SNA) is the study of structural construction based

on graph theory. Characteristics of network structures that describe the pattern of

relationships between individuals, can be applied to epidemiology. The use of graphs in

SNA is to integrate and visualize the network of contacts in order to determine the

potential relationships between contacts, to track who is connected to whom, and how

the connections are formed, so as to map the path of the spread of the disease. In this

paper, the SNA graph provides a description of the contact network in a cluster.

Visualization of the formed SNA graph provides information on the pattern of contact

relationships in the cluster. The measurement of centrality in the SNA method identifies

who has a high value of connectedness and closeness in the cluster.

Keywords: Social Network Analysis, Contact Tracing, SNA Graph.





IC-MaGeStiC 2021

68

Projection Pursuit Regression on Statistical

Downscaling using Artificial Neural Network and

Support Vector Regression Methods

Chandrika Desyana Putri1, Ema Fahma Farikha1, Alfian Futuhul Hadi1*,

Yuliani Setia Dewi1, I Made Tirta1, Firdaus Ubaidillah2, Dian Anggraeni1

1) Data Science Research Group, Department of Mathematics, University of Jember,

Jember 68121, Indonesia 2) Department of Mathematics, University of Jember, Jember 68121, Indonesia *) Corresponding author. Email: [email protected]

ABSTRACT

Information about rainfall is very necessary for the country of Indonesia which bears the

title as an agricultural country. This is because the agricultural sector is very vulnerable

to climate change, where rainfall is one indicator of climate change related to crops.

Therefore, an accurate rainfall forecasting model is needed in order to assist farmers in

determining planting time, cropping patterns and others by utilizing information from

GCM outputs. However, the information provided by GCM is still on a global scale and

has low resolution for local scale forecasting. However, GCM output information can

still be utilized by using statistical downscaling techniques. Statistical Downscaling is a

technique that connects GCM output as a predictor variable with local rainfall in Jember

Regency as a response variable with the intermediary of a functional model. The

response variable, namely local rainfall in Jember, was taken from January 2005 to

December 2018 with a total of 168 data. As for the GCM output response variables,

there are three types of variables used in this study, namely precipitation, sea surface

pressure, and air temperature with a 3×3 domain to a 10×10 domain. The two data will

be split with data from January 2005-December 2017 as training data to build the model

and data from January 2018 to December 2018 as testing data used for model

validation. In this study, rainfall forecasting in Jember Regency was carried out using

two combined methods, the first method was Projection Pursuit Regression followed by

the Artificial Neural Network method. For the second method, using the projection

results from PPR as a dimension reducer of a large predictor variable, namely PP and

followed by the Support Vector Regression algorithm. At the modeling stage with PPR,

the optimum domain and many functions will be determined, where the chosen domain

is a 6×6 domain and the number of optimum functions is m=5. Furthermore, it will be

modeled using two rainfall forecasting methods, namely ANN and SVR. The results of

model validation using RMSE show that the PP+SVR method has a smaller RMSE

value of 65.61 compared to the PPR+ANN method with an RMSE value of 67.48. This

shows that the performance for the PP+SVR model is better than the PPR+ANN model.

Keywords: General Circulation Model (GCM), Statistical Downscaling (SD),

Projection Pursuit (PP), Projection Pursuit Regression (PPR), Artificial Neural Network

(ANN), Support Vector Regression (SVR).



IC-MaGeStiC 2021

69

Correlation Analysis Between The Number Of

Confirmed Cases Of COVID-19 And Stock

Trading In Indonesia

Dinagusti Sianturi1,* Alvida Rukmi2

1,2,3 Department of Mathematics, Institut Teknologi Sepuluh November Campuss ITS,

Sukolilo, Surabaya, 60111, Indonesia


ABSTRACT

The COVID-19 pandemic has impact in every sector of life. Studies of the impact of the

COVID-19 pandemic on stock trading are also being developed in Indonesia regarding

to the number of industries affected by the pandemic. This research aims to provide

information about the results of the correlation analysis between the number of

confirmed cases of COVID-19 in Indonesia and the volume of stock transactions in

Indonesia. From 600 stocks in Indonesia, all of them can be clustered into three cluster

based on their transaction volume using K-Means clustering. Then correlation test is

done between confirmed case of COVID-19 in Indonesia and the transaction volume of

stocks in Indonesia synchronously. From this research found that most stocks in

Indonesia that are classified as having medium and high transaction volumes have direct

correlation with the number of confirmed cases of COVID-19. Or it can be said that the

number of confirmed cases of COVID-19 in Indonesia is increasing, does not causing

stock transactions in Indonesia decrease, but stock transactions in Indonesia is also

increasing.

Keywords: Impact of COVID-19, K-Means, Pearson Correlation Test, Stock Clustering.


IC-MaGeStiC 2021

70

Application of Structural Equation Modelling

(SEM) in Analysis of Performance Determinants

of Multipurpose Cooperatives (KSU) in

Jembrana Regency of Bali of Indonesia

G K Gandhiadi*, K Jayanegara

Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Udayana

University, Denpasar of Bali of Indonesia

*Corresponding author : [email protected]

ABSTRACT Multipurpose Cooperative (KSU) is a cooperative that provides several services at once,

for example selling consumer goods, providing savings and loan services, etc. The

current performance of KSU's business in Jembrana Regency has not been able to play

an optimal role because most of its management is still relatively simple and has not

used the concept of modern entrepreneurship. The factors that influence the

performance of cooperatives are determined by internal factors (participation of

members, entrepreneurial activity and cooperative human resources) and external

factors (the role of the government), which will be used as a reference for the analysis

of KSU's business performance in Jembrana Regency of Bali. The purpose of this

research is to comprehensively analyze the determination of KSU's business

performance in Jembrana Regency. One of the basics of analysis involving latent

variables is Structural Equation Modelling (SEM). The results of this study state that the

total causality of the exogenous construct of social capital and the role of the

government has a positive but not significant effect on the endogenous construct of

business performance and entrepreneurial orientation of KSU managers in Jembrana

Regency of Bali. However, the causality of the entrepreneurial orientation construct has

a positive and significant effect on the business performance of KSU in Jembrana

Regency of Bali. Recommendations to relevant local governments should be more

intense in providing training, providing stimulus and formulating good policies for

improving KSU management and promoting the implementation of social capital

capacity in KSU management in Jembrana Regency of Bali of Indonesia.

Keywords : Business Performance, Multipurpose Cooperative, Social Capital,

Entrepreneurship Orientation, The Role of Government, SEM.



IC-MaGeStiC 2021

71

Random Semi Under Sampling to Increase The

Sensitivity of Imbalanced Data Classification

with Binary Logistic Regression

Gusti Ngurah Adhi Wibawa1, Makkulau, Agusrawati, Irma Yahya

Diploma of Statistics, Vocational Education Program, Halu Oleo University


ABSTRACT

Classification of imbalanced data based on binary logistic regression models usually

gives a low sensitivity value. With oversampling or undersampling the sensitivity value

will usually increase, but accuracy will often decrease. This study aims to determine the

size of the sample that must be taken so that the sensitivity increases but the accuracy

does not decrease significantly. This method is called semi over sampling and semi

under sampling. Data on the birth of 910 babies at the Kendari City Hospital in 2018

related to cases of low birth weight (13%) was used to test the performance of the semi

under sampling and semi over sampling methods by comparing the values of accuracy,

sensitivity, specification, G-mean, Fprate and AUC with non resampling method, over

sampling and under sampling. The simulation results show that the classification of

imbalanced data with 125% semi-under sampling is able to provide a classification

accuracy that is not significantly different from without resampling but the sensitivity

increases about 25%. This value is not significantly different from oversampling. While

the performance of other values is not significantly different among all methods.

Keywords: classification, imbalanced data, binary logistic regression, oversampling,

undersampling,



IC-MaGeStiC 2021

72

Competing Risk Model for Prediction of

Preeclampsia

Nadya Devana1,* Sarini Abdullah1

1 Department of Mathematics, Faculty of Mathematics and Natural Sciences, University

of Indonesia, Depok, 16424, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Preeclampsia (PE) is defined as an obstetrical syndrome with new-onset hypertension

accompanied by the presence of protein in the urine after 20 weeks of gestation.

Preeclampsia/eclampsia is one of the most common causes of perinatal morbidity and

mortality in developing countries. Women diagnosed with PE will have delivery before

or after the development of PE. In this study, we propose a Bayesian competing risk

model to predict the time until deliveries under PE conditions given certain

characteristics of the patients. Data on 946 patients in the first trimester of pregnancy

who gave birth with and without PE condition at the X Hospital Jakarta were used in the

analysis. Bayesian approach was used to create personalized distribution that allowed to

incorporate the expert's opinion in the model, in this case, the clinician's professional

judgment; which in the end was expected to provide a better result than the frequentist

approach. By using Bayesian competing risk model, we expect to identify important

factors explaining the delivery under PE conditions and to produce the probability of

delivery for a specific cause.

Keywords: Bayesian approach, personalized distribution, preeclampsia



IC-MaGeStiC 2021

73

Analysis of Students’ Mathematical Deductive

Reasoning Skill

Uyan Ahmad Satibi1,* Bambang Aviv Priatna2

1,2 Departemen Pendidikan Matematika, Universitas Pendidikan Indonesia, Jl. Dr. Setia

Budhi No 229, Bandung 40154, Indonesia


ABSTRACT

Deductive reasoning skill is part of the mathematical reasoning skill that students must

have in solving mathematical problems. This research was conducted in SMAN 1

Warungkondang. The aims of this research was to determine the skill of students'

mathematical deductive reasoning in solving mathematical problems of derivatives of

algebraic function. The data was collected by means of test and interview. The data

analysis technique uses qualitative data analysis which includes data reduction, data

presentation and drawing conclusions. Based on the results of the research, students

with high deductive reasoning skill reached 20%, students with middle deductive

reasoning skill reached 60% and students with low deductive reasoning skill reached

20%.

Keywords: Deductive Reasoning Skill, Derivative of Algebraic Function, Mathematics.



IC-MaGeStiC 2021

74

The Vector Time Series Analysis on COVID-19

Cases in Bandung City of West Java

U. Mukhaiyar1,*, M. R. Maulana2, and K. N. Sari1

1 Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut

Teknologi Bandung, Indonesia 2 Undergraduate Program in Mathematics, Faculty of Mathematics and Natural

Sciences, Institut Teknologi Bandung, Indonesia


ABSTRACT

The Vector Autoregressive (VAR) is a multivariate time series model which can explain

the interdependency relationship among involved variables. The VAR model is the

generalization of the univariate time series model, namely the Autoregressive (AR).

This model involves more than one stochastic process, thus a process vector is formed.

Those variables could be replaced by observing a variable in some locations. In this

paper, the performance of VAR model is evaluated based on the size of the process

vector. The stationarity and prediction ability of the models be the performance

indicators. As illustration, the COVID-19 positive cases in various districts of Bandung

city be modeled. It is obtained that the size of process vector does not affect the VAR

model performances. Furthermore, the model can be well performed on discrete data

with and without outliers.

Keywords: vector autoregressive, COVID-19, stationarity , three-stage iterative,

prediction.



IC-MaGeStiC 2021

75

SHINY OFFICE-R: a Web-based Data Mining

Tool for Exploring and Visualizing Company

Profiles

I Made Tirta1, Mohamad Fatekurahman2, Khairul Anam3, Bayu Taruna Widjaja Putra4

1,2,3,4 The University of Jember *Corresponding author. Email: [email protected]

ABSTRACT

The profile of institutions or companies are often measured internally, nationally and

internationally using several indicators that may be changed over time. We develop

SHINY OFFICE-R a Web-GUI using R software to explore and visualize data on

institution performance/ profile. Graphical visualization can help a lot in gaining the

insight of the data. The programs are flexible to accommodate different types of

indicators that may be assigned for broad types of institutions and companies. In this

paper we describe the main features of the program and illustrate application of the GUI

using simulated data having various type of performance indicators (say local, national

and international indicators). Furthermore, our web GUI will be available online, so

that it can be easily accessed and applied to explore and visualized the profile of users’

institutions or companies that possibly have different types of indicators.

Keyword: office statistics, performance indicators, graphical visualization, cluster, path

analysis, company profile, Structural Equation Model (SEM), Web


IC-MaGeStiC 2021

76

Naive Bayes Classifier (NBC) For Forecasting

Rainfall In Banyuwangi District Using Projection

Pursuit Regression (PPR) Method

Ana Ulul Azmi1, Alfian Futuhul Hadi2, Yuliani Setia Dewi3, I Made Tirta4,

Firdaus Ubaidillah5, Dian Anggraeni6*

Master of mathematics department, Faculty of Mathematics and Natural Science,

University of Jember, Jember 68121, Indonesia

Email: [email protected],[email protected], [email protected], [email protected], [email protected],

*Corresponding author: [email protected]

ABSTRACT

Rainfall is one of the climates that has a big influence on life, such as aviation,

plantations, and agriculture. Agriculture and plantations in Banyuwangi are mostly

located in remote areas. Remote areas are most likely to lack information on weather

and climate data. Rainfall information in the future is also very decisive for the

community in carrying out their daily lives, therefore prediction models or rainfall

forecasting are very necessary for the community. This situation has encouraged the

development of various models of approaches for forecasting rainfall. One approach for

forecasting rainfall is the use of Global Circulation Model (GCM) data. GCM resolution

is too low to predict local climate which is influenced by topography and land use, but it

is still possible to use GCM to obtain local scale information if Statistical Downscaling

(SDs) technique is used. SDs is a technique that connects GCM output as a predictor

variable with local rainfall in Banyuwangi Regency as a response variable with an

intermediary functional model. The response variable, namely local rainfall in

Banyuwangi Regency, was taken from January 2011 to December 2020 with a total of

120 data. As for the GCM output response variable, there are three types of variables

used in this study, namely rainfall, sea level pressure, and air temperature with a domain

of 3×3 to 10×10. Forecasting rainfall in Banyuwangi Regency is carried out using the

Projection Pursuit Regression (PPR) method. At the modeling stage with PPR, the

optimum domain and many functions will be determined, where the chosen domain is

the 6×6 domain and the optimum number of functions is m=6. The results of model

validation using RMSE show that the PPR method has an RMSE value of 89.79. A

process is needed that can represent the results of forecasting in the form of numbers

into something that is more understandable to the public. This process is the

classification of rainfall data. The classification method used in this study is the Naive

Bayes Classifier (NBC). Rainfall class is divided into 4, namely dry months, humid

months, wet months, and very wet months. The testing data used for the NBC

classification are the last 24 data. Meanwhile, NBC uses PPR as a model that produces

classification forecasts with correct values for 18 months out of a total of 24 months.


IC-MaGeStiC 2021

77

The correct values consist of 8 wet months, 3 wet months and 7 very wet months. The

confusion matrix produces an accuracy rate of 75%.

Keywords: General Circulation Model (GCM), Statistical Downscaling (SDs),

Projection Pursuit Regression (PPR), classification, Naive Bayes Classifier (NBC).


IC-MaGeStiC 2021

78

Statistical Downscaling Technique Using

Response Based Unit Segmentation-Partial Least

Square (REBUS-PLS) for Monthly Rainfall

Forecasting

Izdihar Salsabila1, Alfian Futuhul Hadi1*, I Made Tirta1, Yuliani Setia

Dewi1, Firdaus Ubaidillah2, Dian Anggraeni1

1) Data Science Research Group, Department of Mathematics, University of Jember,

Jember 68121, Indonesia 2) Department of Mathematics, University of Jember, Jember 68121, Indonesia *) Corresponding author. Email: [email protected]

ABSTRACT

The availability of climate information is an important issue in various fields. Climate

change that fluctuates erratically requires the availability of models or methods to

provide accurate climate information. Forecasting is the prediction of the values of a

variable to a known value of the variable or related variables. One of the newest

forecasting techniques today is the Statistical Downscaling (SD) technique. The SD

technique is a procedure for inferring high-resolution information from low-resolution

variables. Forecasting rainfall using SD technique is to build a function that can predict

the value of a response variable using predictor variables, for the example the variables

in the Global Circular Model (GCM). In this study, forecasting will be carried out using

the Partial Least Square (PLS) model and compared with the PLS model that has been

time segmented namely REBUS-PLS model. This study uses four latent variables

consisting of three exogenous latent variables and one endogenous latent variable. The

exogenous variable ξ1 is precipitation, ξ2 is air pressure, and ξ3 is temperature, while the

endogenous variable is monthly rainfall. The measurement model is a functional rule

that describes the mathematical relationship between exogenous latent variables ξ1, ξ2,

and ξ3 with their corresponding manifests. After obtaining the structural model and

measurement model, then parameter estimation is carried out. The result is that the three

exogenous latent variables have a very significant effect on the endogenous latent

variable . The PLS model obtained was then tested for the goodness of the model with

several indicators, namely R2, mean redundancy, and Goodness of Fit. With the values

obtained are 70.05%, 49.098% and 76.11%. Time segmentation in REBUS is done by

classifying the training data using the Schmidt Ferguson classification. There are 4

segmentations and the results of the data segmentation which are included in segment 1,

segment 2, segment 3, and segment 4 are 33 months, 29 months, 50 months, and 32

months. The validity and reliability tests were carried out again in each segment.

Furthermore, the goodness of the model is also tested on each local model. The R-



IC-MaGeStiC 2021

79

square values generated in segment 1, segment 2, segment 3, and segment 4 are 97.13%,

97.52%, 85.05%, and 91.38%. Meanwhile, the mean redundancy for each segment is

61.58%, 55.60%, 70.29%, and 61.97%. The last indicator is Goodness of Fit (GoF). The

local GoF values of the model in each segment are 87.8%, 86.4%, 82.8%, and 84.5%.

Rainfall forecasting on January 2017 to December 2017 data is carried out as the final

stage to test the capabilities of the PLS and REBUS-PLS models. Overall, the PLS

model has a smaller RMSE than the REBUS-PLS model at 25 observation stations.

Meanwhile, at the other 52 observation stations, the accuracy of the REBUS-PLS model

is better than the PLS model.

Keywords: General Circulation Model (GCM), Statistical Downscaling (SD), Partial

Least Square (PLS), Rsponse Based Unit Segmentation-Partia Least Square (REBUS-

PLS)


IC-MaGeStiC 2021

80

Statistical Literacy Ability In Term Of Adversity

Quotient

Iffa Hanifah Rahman1,* Aan Hasanah2

1 Department of Mathematics Education, School of Postgraduate Studies, Universitas

Pendidikan Indonesia 2 Department of Mathematics Education, Universitas Pendidikan Indonesia *Email: [email protected]

ABSTRACT

Statistical literacy is an ability that every student needs to have in facing the challenges

of the 21st century. Many students have poor statistical literacy skills, because each

student has different response in responding challenge that can be called the adversity

quotient. Adversity quotient is divided into three types, namely climber, camper, and

quitter types. This study aims to analyze the statistical literacy skills of junior high

school students which are refilled based on the indicators of adversity question that have

been compiled. This research uses descriptive qualitative research. The subjects in this

study were ninth grade students of junior high school. Data collection techniques using

adversity quotient questionnaires, test questions and interviews. The results of this study

indicate that adversity quotient has an influence on the achievement of students'

statistical literacy skills. Students who have the climber type are able to meet all

indicators of statistical literacy skills.

Keywords: Statistical Literacy, Adversity Quotient, Qualitative Research.


IC-MaGeStiC 2021

81

Weather Forecasting at BMKG Office,

Lumajang City Using Markov Chain Method

Ummi Masrurotul Jannah

Magister Mathematics, Mathematics Department, Faculty of Mathematics and Sciences,

University of Jember, Indonesia


ABSTRACT

Weather forecasting is one of the important factors in everyday life, because it can

affect the activities carried out by the community. Weather forecasting refers to a

series of activities carried out to produce a set of information about weather

conditions. One method that can be used to model these uncertain conditions is the

Markov chain. The Markov chain is a random process in which all information

about the future is contained in the present state. In this study the authors use daily

weather data that occurs on January 3-4.

Keywords: Weather forecasting, Markov chain


IC-MaGeStiC 2021

82

Comparison of Kriging and Neural Network

Methods in Interpolation of Rainfall

Novi Nur AINI1, Atiek IRIANY*1, Waego Hadi NUGROHO1

1 Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya

University, Malang, Indonesia *Corresponding author. Email: [email protected]

ABSTRACT

Rainfall is an important aspect in determining the start of the season, both the rainy

season and the dry season. The availability of complete rainfall data in an area is

needed. Rainfall observation points in Indonesia are still limited, so a method is needed

to predict rainfall in locations where there are no observation points. Several methods

can be used to estimate rainfall values in unobserved locations, namely by using kriging

interpolation and neural networks. Both methods can be used to interpolate data by

utilizing spatial information. The kriging method used is the ordinary kriging method.

The neural method uses a backpropagation neural network architecture. The purpose of

this study was to compare the interpolation values observed with the ordinary kriging

and backpropagation neural network methods. The results of the interpolation with

these two methods show that the interpolation of rainfall using the neural network

method provides better performance than the ordinary kriging method. This is indicated

by the smaller RMSE value.

Keywords: Interpolation, Kriging, Neural Network, Rainfall


IC-MaGeStiC 2021

83

Classification of Bank Deposits Using Naive

Bayes Classifier (NBC) and K–Nearest Neighbor

(K-NN)

M H Effendy1, D Anggraeni2*, Y S Dewi3, A F Hadi4

1,2,3,4 Departement of Mathematics, Faculty of Mathematics and Natural Science, University of Jember,

Jember, 68121, Indonesia. 2,3,4 Data Science Research Group, Departement of Mathematics, Faculty of Mathematics and Natural

Science, University of Jember, Jember, 68121, Indonesia.

*corresponding author: [email protected].

ABSTRACT

Banks are financial institutions whose activities are to collect funds from the public in

the form of deposits (saving deposit, demand deposit, and time deposit) and distribute

them to the public in the form of credit or other forms. Deposits are an alternative for

customers because the interest offered on deposits is higher than regular savings. Naïve

Bayes Classification (NBC) is a statistical classification method based on Bayes'

theorem that can be used to predict the probability of membership of a class. K-Nearest

Neighbor (K-NN) is also a method for classifying objects based on the learning data

that is closest to the object. This study will use bank customer data consisting of 4521

records and 17 variables. The results of this study indicate that the K-NN method is

better than the NBC method. K-NN gives the best performance of both accuracy and

sensitvity. Both method showed the same results to get the importance variables. From

16 variables in classifying banking customers, there are top 5 variables that have the

most influence to customer to decide whether to join a time deposits or not. That 5

variables are the duration of time the bank contacted its customers (duration), the results

of the previous deposit offer (poutcome), the last month contacted the customer

(month), the type of communication used by the customer (contact), and the number of

contacts the bank had made prior to the promotion of opening a deposit (previous).

Keywords: classification, Naive Bayes Classifier, K-Nearest Neighbor, importance

variables