JURNAL DAN PENELITIAN TEKNIK INFORMATIKA

Volume 5 | Number 1 | October 2020

E-ISSN : 2541-2019P-ISSN : 2541-044X

JURNAL DAN PENELITIAN TEKNIK INFORMATIKA

Dipublikasi oleh :

PREFACE

Praise the presence of Allah SWT, for blessings and mercy, in the successful

publication of the Journal SinkrOn Volume 5 Number 1 for the October 2020 Period which

refers to the rule for journal writing determined by Polytechnic Ganesha Medan.

SinkrOn as a media publication and a place to share scientific work in the field of

Informatics Engineering which is published twice in a year, namely in October and April,

and this edition is filled with contribution from a various institution in parts of Indonesia

with research fields related to the field of Informatic Engineering from lectures and graduate

students.

In this edition and beyond, SinkrOn is committed to continuously improving the

quality of journals to be published. SinkrOn substantially tightens the entire review process

to publish quality journals. Currently, SinkrOn has been accredited by RistekBRIN Sinta 3

and indexed DOAJ. SinkrOn has the vision to continue to improve accreditation to reach

Sinta 1 in the next few years. Besides, SinkrOn also has the vision to enter the realm of

international journals with the hope of reaching writers and readers from all over the world.

In this edition, Sinkon has also involved several editors and reviewers from various countries

such as Indonesia, Malaysia, India, Iraq, and Italy.

Our deepest gratitude to the authors, reviewers, editors, and all parties involved in

publishing this journal. Hopefully, this journal can provide good benefits for all academics

in the field of Informatics Engineering and still waiting for your brilliant work in the next

edition.

Medan, 27 November 2020

Editor in Chief

Thanks to Reviewers

Mohammad Andri Budiman (Universitas Sumatera Utara, Indonesia)

Dian Rachmawati (Universitas Sumatera Utara, Indonesia)

Sriadhi (Medan of University, Medan, Indonesia)

Dicky Apdillah (Asahan Univercity, Indonesia)

Ida Bagus Irawan Purnama (Politeknik Negeri Bali, Indonesia)

Oman Somantri (Politeknik Negeri Cilacap, Indonesia)

Syahrul Mauluddin (Universitas Komputer Indonesia, Indonesia)

Addin Aditya (STIKI Malang, Indonesia)

Anjar Wanto (STIKOM Tunas Bangsa)

Anak Agung Gde Satia Utama (Universitas Airlangga)

Silvester Dian Handy Permana (Universitas Trilogi)

Alyaa Abdul-Moneim Mohammed (University of Babylonia, Iraq)

I Ketut bayu Yogha Bintoro (Universitas Trilogi, Indonesia)

Castaka Agus Sugianto (Politeknik TEDC, Bandung, Indonesia)

Nitin Jain (Galgotias University, Greater Noida, India)

Siti Mutrofin (Universitas Pesantren Tinggi Darul Ulum, Indonesia)

Nihad Ibrahim Abdullah (Universitas Tekniknal Malaysiam Melaka)

Mesran (Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma, Medan, Indonesia)

Tengku Mohd Diansyah (Universitas Harapan, Medan, Indonesia)

Table of Contents

Preface

Thanks to Reviewers

Accreditation Certificate

Table of Contents

Business Development Management Model at Samo-Samo Recycling House Based

on SWOT Analysis

Rusdiansyah Rusdiansyah, Harun Al Rasyid, Suryanto Sosrowidigdo

1-6

Expert System for Diagnosing Pregnancy Complaints by Forward Chaining

Embun Fajar Wati, Anggi Puspitasari

7-16

Implementation of Apriori Algorithm Data Mining for Increase Sales

Reza Alfianzah, Rani Irma Handayani, Murniyati Murniyati

17-25

Drug Stock Prediction at Balige HKBP Hospital Using Adaptive Neuro-Fuzzy

Inference System

Arie Satia Dharma, Lily Andayani Tampubolon, Daniel Somanta Purba

26-34

The Implementation of Artificial Intelligence in Charity Box at Mosque and

Musholla as RFID Based Security System

Defnizal Defnizal, Risa Nadia Ernes

35-42

Facial Recognition Implementation using K–NN and PCA Feature Extraction in

Attendance System

Juliansyah Putra Tanjung, Bayu Angga Wijaya

43-50

Data Mining Model For Designing Diagnostic Applications Inflammatory Liver

Disease

Omar Pahlevi, Amrin Amrin

51-57

Prediction of Netizen Tweets Using Random Forest, Decision Tree, Naïve Bayes, and

Ensemble Algorithm

Yan Rianto, Antonius Yadi Kuntoro

58-71

Expert System for Monitoring Elderly Health Using the Certainty Factor Method

Linda Marlinda, Widiyawati Widiyawati, Reni Widiastuti, Wahyu Indrarti

72-77

An Expert System Diagnosis Human Eye Diseases Using Certainty Factor Method

Web-Based

Bayu Angga Wijaya, Juliansyah Putra Tanjung

78-83

Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm

Jarseno Pamungkas, Yopi Handrianto

84-91

Designing an Arduino-based Automatic Cocoa Fermentation Tool

Balkhaya, Dirja Nur Ilham, Rudi Arif Candra, Hasbaini Hasbaini, Fera Anugreni

92-99

Design of an Automatic Water Pump on a Traditional Boat

Ihsan Ihsan, Dirja Nur Ilham, Rudi Arif Candra, Amsar Yunan, Hardisal Hardisal

100-106

Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes

Classifier Algorithm

Miftahul Kahfi Al Fath, Arini Arini, Nashrul Hakiem

107-115

The K-Medoids Clustering Method for Learning Applications during the COVID-19

Pandemic

Samudi Samudi, Slamet Widodo, Herlambang Brawijaya

116-121

Credit Loan Selection During the Pandemic Recommendation MCDM-Promethee

Method

Akmaludin, Erene Gernaria Sihombing, Linda Sari Dewi, Rinawati Rinawati, Ester

Arisawati

122-128

The Infusion of Notification Design With an Application of Social Media Based on a

Internet of Things (IOT)

Rudi Arif Candra, Devi Satria Saputra, Dirja Nur Ilham, Herry Setiawan, Hardisal

129-137

Decision Support System For Determining Exemplary Students Using SAW Method

Adjat Sudradjat, Henny Destiana, Aprilah Amira Sefenizka

138-145

A Geographic Information System for Managing and Mapping Irrigation

Infrastructure

Rachmat Wahid Saleh Insani, Syarifah Putri Agustini

146-151

Web-Based Desktop Support Trouble Ticket System Design In PT. Mnc Mediacom

Cable

Besus Maulana Shulton, Eva Zuraidah

152-163

Period Study Accuracy Prediction using Sequential Minimal Optimization Algorithm

Hendri Noviyanto, Bayu Mukti

164-169

Twitter Comment Predictions on Dues Changes BPJS Health In 2020

Riza Fahlapi, Yan Rianto

170-183

Sinkron : Jurnal dan Penelitian Teknik Informatika Volume 5, Number 1, October 2020

DOI : https://doi.org/10.33395/sinkron.v5i1.10649

e-ISSN : 2541-2019 p-ISSN : 2541-044X

*Corresponding Author

This is an Creative Commons License This work is licensed under a Creative

Commons Attribution-NonCommercial 4.0 International License. 116

The K-Medoids Clustering Method for

Learning Applications during the COVID-19

Pandemic

Samudi1)*, Slamet Widodo2), Herlambang Brawijaya3) 1)STMIK Nusa Mandiri,Indonesia, 2,3)Bina Sarana Informatika University, Indonesia

1)[email protected], 2)[email protected], 3)[email protected]

Submitted : Sep 21, 2020 | Accepted : Oct 8, 2020 | Published : Oct 8, 2020

Abstract: The central government took a policy to carry out social distancing with

this social distancing impact on all activities such as impacting the learning process

that usually takes place in class turning into online learning which uses several

supporting applications in the learning process during the Covid 19 pandemic. Such

as using WhatsApp, Moodle, and Zoom for the learning process. Of the three

applications commonly used by students and lecturers for the learning process, they

can be grouped using the K-Medoids method which will get the preferred and

disliked application clusters. Researchers took data from questionnaires made with

Google Form which were distributed to 100 students who often did the learning

process with these three applications. Can produce groupings of applications that are

liked and disliked by students in the learning process. By looking at the level curve

in fig 7. There are two clustering in red and blue. If the curve line to the right is

increasing, many things are not liked in the application used. Conversely, if the line

from the curve towards the right decreases, the less preferred online applications.

The results of this study can be seen from the curve image that learning is the most

preferred in the learning process during the Covid 19 pandemic using the Moodle

and WhatsApp applications, and for the use of zoom is not preferred in the learning

process.

Keywords: Data Mining, K-Medoid Algorithm, Clustering, Learning, Covid 19

INTRODUCTION

All countries are currently experiencing a phenomenon that is very dangerous to health, namely the attack of

COVID 19 or commonly called the Corona Virus, one of which is the State of Indonesia which consists of several

islands that cannot be separated from the attack of COVID 19. COVID 19 is one of the diseases that first appeared

in Wuhan City, Hubei Province, China at the end of December 2019 (Etikasari, Puspitasari, Kurniasari, &

Perdanasari, 2020). Until now, there has not been found a drug or vaccine that can be used to overcome the spread

of the COVID 19 pandemic (Arianto & P, 2020). In Indonesia, the addition of new cases As of August 20, 2020,

an increase of 2,266 cases, while the number of patients recovering from COVID 19 has exceeded the number

100,674 cases or 68.3% (Tim Komunikasi Komite Penanganan Corona Virus Disease 2019 (Covid-19) dan

Pemulihan Ekonomi Nasional, 2020).

For this reason, the central government continues to urge everyone to implement social distancing to minimize

the transmission of COVID 19. Social distancing itself is an action where everyone is required not to be close to

one another (Haqien & Rahman, 2020). With social distancing, all activities hurt all fields (Dwitri, Tampubolon,

Prayoga, Zer, & Hartaman, 2020), one of which is the learning process at the school to college-level must be done

at home. Therefore, the learning process carried out at home requires learning media that is affordable online

(Chesilia, Oktaviany, & Dewi, 2012), the challenge is determining the right platform (Yulianto, Cahyani, &

Silvianita, 2020) to support the learning process during the COVID pandemic. 19.

The learning process that takes place in tertiary institutions that researchers have done Online, is what

researchers often do for the learning process with three applications such as WhatsApp, zoom, and Moodle. With

the three applications online, researchers feel.

Based on the constraints in determining applications to support learning during a pandemic, it can be grouped

using the K-Medoids algorithm, grouping or what is commonly called clustering based on previous researchers is

a technique used for statistical data analysis in various fields, one of which is data mining (Madhulatha TS, 2012)

https://doi.org/10.33395/sinkron.v5i1.10649

mailto:[email protected]



e-ISSN : 2541-2019 p-ISSN : 2541-044X



and aims to share data in clusters (Mustakim, 2017). In making clusters, K-Medoids can use because they have a

lot of information on the cluster creation process (Tamura & Miyamoto, 2014).

In this study, the researchers applied the K-Medoids algorithm in grouping the use of applications for learning

during the COVID 19 pandemic from various applications that could support the learning process, so researchers

wanted to find out information about the media used from various platforms such as zoom, WhatsApp, and Moodle

applications which is often used for learning media is it acceptable during the learning process.

LITERATURE REVIEW Medoid is better than K-Means because in K-Medoids we find k as a representative object to minimize the

number of inequalities of data objects, whereas in K-Means we use the total Euclidean distance for data objects

(Marlina, Putri, Fernando, & Ramadhan, 2018). Clustering is a technique used for statistical data analysis, in

various fields, one of which is data mining (Madhulatha TS, 2012) and aims to divide into clusters (Sabzi A,

Farjami Y, 2011) with a similarity level (Irwansyah & Faisal, 2015). Grouping using the K-Medoids method is

widely proposed by several researchers and can be applied such as clustering the spread of COVID 19 in Indonesia

(Sindi et al., 2020), which results in grouping the spread of COVID 19 for the community. And there is another

researcher for the K-Medoids algorithm for disease grouping in Pekanbaru Riau (Juninda, Mustakim, & Andri,

2019) which produces 4 clusters. The steps for the K-Medoids algorithm are as follows (Pramesti, Lahan, Tanzil

Furqon, & Dewi, 2017)

1. Initialize k cluster centers (number of clusters)

2. Allocate each data (object) to the nearest cluster using the Euclidian Distance measurement equation with the

equation:

3. Randomly select objects in each cluster as candidates for the new medoid.

4. Calculate the distance of each object in each cluster with the new medoid candidate.

5. Calculate the total deviation (S) by calculating the value of the new total distance - the old total distance. If S

< 0, then swap objects with cluster data to form a set of k new objects as medoids.

6. Repeat steps 3 to 5 until there is no change in medoids so that you get the cluster and the members of each

cluster.

METHOD

The method used in this study is from identifying problems that exist at this time, then by collecting data, after

the data is collected, the initial data processing is then divided into several categories, then the application of the

K-Medoids algorithm by entering data into the rapid miner application the last is the evaluation of the results of

the data that has been inputted to the rapid miner, then a diagram will appear that can provide information related

to data grouping using K-Medoids. You can also see the sequence based on the previous researcher diagram in Fig

1 below (Juninda et al., 2019).

Start

Identification of problems

Data collection

Initial Data Processing

Application of the K-Medoids Algorithm

Results Evaluation

Finish

Source : (Juninda et al., 2019)

Fig. 1. Research methods




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RESULT

Identification Problem

Researchers took three applications that would be grouped based on the learning process that was liked and

disliked in the learning process during the COVID 19 pandemic. The three applications that the researchers used

in this study were Moodle, Zoom, and Whatsapp.

Data Collection

In collecting data the researchers used an online questionnaire using a google form which was addressed to

100 students who used to carry out the teaching and learning process online with WhatsApp, Zoom, and Moodle.

Initial Data Processing

The data processing that the researchers did was divided into two categories of likes and dislikes from the three

Zoom, Moodle, and Whatapp applications used in the learning process during the COVID 19 pandemic as follows:

Table 1.

Application Users

Application Like Dislike

Moodle 11 16

Zoom 36 68

WhatsApp 53 16

Source : Research Result (2020)

Application of the K-Medoids Algorithm

The data that has been obtained, the researcher enters the Rapid Miner software by selecting New Process as

follows.

Fig. 2. Rapid Miner

After that, a worksheet consisting of the left middle, and right columns will appear. To enter data from Excel,

select the lower left part where there is a sign that the import data into an existing repository is as follows:

Fig. 3. Entry Data

After entering data from Excel then click next so that it will display the type attribute that will be used in the

grouping process in Rapid Miner. Several things must be changed in determining attributes, including changing

the application attribute to nominal and changing it to id as follows:




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Fig 4. Attribute Selection

Click next, then save it with the name Learning Data With Applications in the COVID Period. After saving,

the next step is to enter the K-Medoids into the worksheet on the top left in the modeling, clustering, and

segmentation section. Then move the worksheet, then the Excel data set is also moved to the worksheet, then draw

a line so that it forms as below:

Fig. 5. Inserting K-Medoids

After that, the next step is the kiln run which has the blue triangle symbol above, a data view, a metadata view,

a plot view, advanced charts, and annotations will appear. So that the formation of cluster 0 and cluster 1 will

appear, for cluster 0 consists of moodle and WhatsApp, for cluster 1 consists of zoom. The display is as follows:

Fig 6. Cluster formation

To be clearer in grouping likes or dislikes in the learning process using K-Medoids, it can be seen in Figure 7

to show it, you can choose the Cluster Model (Clustering) so that it will be clear that the ascending and descending

curves will be seen, such as cluster 0 which consists of Moodle and WhatsApp with a blue stripe showing that the

more to the right, the smaller is not liked, unlike cluster 1 which consists of zoom, the higher is not liked.




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Fig 7. Cluster Model (Clustering)

5. Evaluation of Results In the evaluation and the results in this fifth stage, it can be seen the results of the clustering model that is formed in the rapid miner which has been described above that grouping with K-Medoids can form 2 clusters.

DISCUSSION

By clustering online applications such as WhatsApp, zoom, and moodle which are often used in the learning

process for 100 students, it can produce a grouping of applications that students like and dislike in the learning

process. By looking at the level curve in fig 7. There are 2 clusterings in red and blue. If the curve line to the right

is increasing, many things are not liked in the application used. Conversely, if the line from the curve tow

CONCLUSION

Based on the results of the research, the authors can conclude that during the COVID-19 pandemic that spreads

throughout the world, wherein the learning process that is usually delivered directly meeting one another must be

replaced by a social distancing system The lessons that are delivered must be presented online with a variety of

applications used, such as zoom, WhatsApp or moodle. In this study, the results show that the clustering or

grouping that has been carried out on the rapid miner results that learning is the most preferred in the learning

process during the current COVID 19 pandemic using the Moodle and WhatsApp applications, for the use of zoom

is not preferred in the learning process. For future research, we can examine the factors that influence the

application in learning during a pandemic which are the most liked and disliked.

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