JISKA (Jurnal Informatika Sunan Kalijaga) 2021-05-03T11:04:17+07:00 Muhammad Taufiq Nuruzzaman Open Journal Systems <p><span lang="en"><strong><strong><strong>Jurnal Informatika Sunan Kalijaga</strong></strong> (JISKa) </strong>was formed in 2016 by the Informatics Engineering Program of UIN Sunan Kalijaga Yogyakarta as a forum for the publication of research results from lecturers, researchers, students, and practitioners in the field of Informatics / Computer Science and their interconnections with Religion and other sciences. JISKa is published three times a year in <strong>January</strong>, <strong>May</strong>, and <strong>September</strong> and contains <strong>seven articles</strong> each time. JISKa is free and open access for all writers and readers.</span></p> <p>JISKa has been <a href=""><strong>Sinta 4</strong></a> accredited in 2018-2023. The decree can be downloaded <a href="">here</a>. JISKa has also been indexed by <a title="DOAJ" href="" target="_blank" rel="noopener">DOAJ</a>, <a href="">Garuda</a>, <a href="">Moraref</a>, <a href=";hl=id">Google Scholar</a>, and <a href="">Dimensions</a>. Please open the link on the Sinta, Garuda, Moraref, and Google Scholar banners to see the impact generated by articles published by JISKa.</p> <p>JISKa is open to receiving any time articles from authors. Please register for an account and download the JISKa template file for submitting articles to JISKa. Each article will be reviewed by a minimum of <strong>two reviewers</strong> with a blind review method where the reviewers can find out the identity of the author but not vice versa. Detailed writing rules and examples can be seen in the JISKa template file. Until now, JISKa only uses the <a title="Template JISKa" href="" target="_blank" rel="noopener">MS DOC / DOCX format</a> for article submissions.</p> <p> </p> <p><span lang="en"><strong>Jurnal Informatika Sunan Kalijaga (JISKa)</strong> dibentuk tahun 2016 oleh Teknik Informatika UIN Sunan Kalijaga sebagai wadah dan forum publikasi hasil penelitian dari dosen, peneliti, mahasiswa, dan praktisi di bidang Informatika/Ilmu Komputer dan integrasi-interkoneksinya terhadap Agama dan keilmuan lain. JISKa terbit setahun tiga kali pada bulan <strong>Januari</strong>, <strong>Mei</strong>, dan <strong>September </strong>berisi <strong>tujuh artikel</strong> setiap kali terbit. JISKa bersifat <em>free and open access</em> di mana penulis dan pembaca artikel tidak dibebani biaya. </span></p> <p>JISKa sudah terakreditasi <a title="Sinta" href="" target="_blank" rel="noopener"><strong>Sinta 4</strong></a> sejak tahun 2018 s.d. 2023. Surat Keputusan dapat diunduh di <a title="SK Sinta4" href="" target="_blank" rel="noopener">sini</a>. JISKa juga telah diindeks oleh <a title="DOAJ" href="" target="_blank" rel="noopener">DOAJ</a>, <a title="Garuda" href="" target="_blank" rel="noopener">Garuda</a>, <a title="Moraref" href="" target="_blank" rel="noopener">Moraref,</a> <a title="Google Scholar" href=";hl=id" target="_blank" rel="noopener">Google Scholar</a>, dan <a title="Dimensions" href="" target="_blank" rel="noopener">Dimensions</a>. Silakan membuka link pada banner Sinta, Garuda, Moraref, dan Google Scholar untuk melihat <em>impact</em> yang dihasilkan oleh artikel-artikel yang telah dipublikasikan oleh JISKa. </p> <p><span lang="en">JISKa terbuka untuk menerima artikel sepanjang waktu dari para penulis. Silakan mendaftar akun (<em>register</em>) dan mengunduh file template JISKa untuk pengajuan artikel di JISKa. Setiap artikel akan direview oleh minimal <strong>dua mitra bestari (<em>reviewer</em>)</strong> dengan metode <em>blind review</em> di mana mitra bestari bisa mengetahui identitas penulis namun tidak untuk sebaliknya. Tata aturan penulisan secara detil disertai contoh dapat dilihat pada file template JISKa. Sampai saat ini, JISKa hanya menggunakan <a title="Template JISKa" href="" target="_blank" rel="noopener">format MS DOC/DOCX</a> untuk pengajuan artikel. </span></p> Segmentasi Pelanggan Berdasarkan Perilaku Penggunaan Kartu Kredit Menggunakan Metode K-Means Clustering 2021-04-18T15:23:35+07:00 Fatimah Defina Setiti Alhamdani Ananda Ayu Dianti Yufis Azhar <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p><em>Credit card is one of the payment media owned by banks in conducting transactions. Credit card issuers provide benefits for banks with interest that must be paid. Credit card issuers also provide losses to banks that have agreed to pay not to pay their credit card bills. To request a loan from the bank, a cluster model is needed. This study, proposing a segmentation system in research using credit cards to determine marketing strategies using the K-Means Clustering method and conducting experiments using the 4 methods namely K-Means, Agglomerative Clustering, GMM, and DBSCAN. Clustering is done using 9000 active credit card user data at banks that have 18 characteristic features. The results of cluster quality accuracy obtained by using the K-Means method are 0.207014 with the number of clusters 3. Based on the results obtained by considering 4 of these methods, the best method for this case is K-Means.</em></p> </div> </div> </div> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Fatimah Defina Setiti Alhamdani, Ananda Ayu Dianti, Yufis Azhar Analisis Sentimen Review Halodoc Menggunakan Nai ̈ve Bayes Classifier 2021-04-18T15:24:03+07:00 Asep Hendra Fitriyani Fitriyani <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p><em>Healthcare service has the role to help and serve people to access medical services, i.e. providing medicines, medical consultation, or health control. Healthcare service has been transforming to a digital platform. Halodoc is one of the digital platforms that people can use for free or paid, user can also give reviews of Halodoc’s performance and services on Google Play Store to give feedback that Halodoc can use to evaluate and improve the app. The Google Play Store review is increasing every day. Therefore an analysis for the review with sentiment analysis for Halodoc’s review is needed, first phase of sentiment analysis for the review is preprocessing which has tokenization, transform to lower cases, filter stopword, dan filter token (by length) processes. The data is divided into two positive and negative classes with cross-validation and a k-fold validation value of 10, using Naïve Bayes Classifier algorithm with 81,68% accuracy and AUC 0.756, categorized as fair classification.</em></p> </div> </div> </div> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Asep Hendra, Fitriyani Fitriyani Prediksi Barang Keluar TB. Wijaya Bangunan Menggunakan Algoritma KNN Regression dengan RStudio 2021-04-18T15:24:37+07:00 Natcha Kwintarini Suparman Budi Arif Dermawan Tesa Nur Padilah <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p><em>TB. Wijaya Bangunan is a business entity that has weaknesses in managing inventories. This study aims to help TB. Wijaya Bangunan in managing inventory based on existing data reduce the difference between the number of incoming goods and the number of outgoing goods. The methods used are data collection, data preparation, data selection, preprocessing, data transformation, distance calculation, calculation of predictions, evaluation, and display of prediction results using a Shiny framework. This study uses the Time Series KNN Regression algorithm to predict the number of outgoing goods based on time series data with existing data. The most predicted results came out in the 9th week period as much as 22.40%. Based on the process that has been done, it can be concluded that the evaluation value of Root Mean Square Error (RMSE) is at least 3.55, which means it has the best predictive accuracy results.</em></p> </div> </div> </div> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Natcha Kwintarini Suparman, Budi Arif Dermawan, Tesa Nur Padilah Metode Accumulative Difference Images untuk Mendeteksi Berhentinya Putaran Kincir Air 2021-04-18T15:25:17+07:00 Adri Priadana Aris Wahyu Murdiyanto <p><em>Vannamei shrimp is one of Indonesia's fishery commodities with great potential to be developed. One of the essential things in shrimp farming is a source of dissolved oxygen (DO) or a sufficient amount of oxygen content, which can be maintained by placing a waterwheel driven by a generator set engine called a generator. To keep the waterwheel running, the cultivators must continue to monitor it in real-time. Based on these problems, we need a method that can be used to detect the cessation of waterwheel rotation in shrimp ponds that focuses on the rotation of the waterwheel. This study aims to analyze the performance of the Accumulative Difference Images (ADI) method to detect the stopped waterwheel-spinning. This method was chosen because compared with the method that only compares the differences between two frames in each process, the ADI method is considered to reduce the error-rate. After all, it is taken from the results of the value of several frames' accumulated movement. The ADI method's application to detect the stopped waterwheel-spinning gives an accuracy of 95.68%. It shows that the ADI method can be applied to detect waterwheels' stop in shrimp ponds with a very good accuracy value.</em></p> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Adri Priadana, Aris Wahyu Murdiyanto Analisis Hashtag pada Twitter untuk Eksplorasi Pokok Bahasan Terkini Mengenai Business Intelligence 2020-12-08T04:53:54+07:00 Arif Himawan Muhammad Rifqi Maarif Ulfi Saidata Aesyi <p><em>The main purpose of this paper is to examine the dominant topics about Business Intelligence in micro-blogging Twitter. There are 7.153 tweets collected from Twitter API. Text mining and natural language processing are used to analyze the dominant topics among those tweets. Computational method used to count the most frequent hashtag that appears together with Business Intelligence hashtag. Twitter users are large and scattered around the world with a diverse range of skills (expertise) that can give a new perspective on a subject that may not be predicted before. For example, for topics related to Business Intelligence, the very dominant general topic discussed in the scientific literature are about data management, as well as for analytics and machine learning data. The result contributes to understanding dominant topics about Business Intelligence that can help researchers to level their research.</em></p> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Arif Himawan, Muhammad Rifqi Maarif, Ulfi Saidata Aesyi Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0 2020-11-19T11:21:12+07:00 Ulfi Saidata Aesyi Alfirna Rizqi Lahitani Taufaldisatya Wijatama Diwangkara Riyanto Tri Kurniawan <em><span lang="IN">The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.</span></em> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Ulfi Saidata Aesyi, Alfirna Rizqi Lahitani, Taufaldisatya Wijatama Diwangkara, Riyanto Tri Kurniawan Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia 2021-01-24T17:40:33+07:00 Nadhif Ikbar Wibowo Tri Andika Maulana Hamzah Muhammad Nur Aini Rakhmawati <p class="Default"><em>Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score </em><em>0.503583. SVM is the best performing classifier.</em></p> 2021-05-03T00:00:00+07:00 Copyright (c) 2021 Nadhif Ikbar Wibowo, Tri Andika Maulana, Hamzah Muhammad, Nur Aini Rakhmawati