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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Informatics and Data Science Bulletin of Artificial Intelligence
Mustakim Mustakim
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : building of informatics technology and science

 1 
Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau Adyah Widiarni; Mustakim Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.346 KB) | DOI: 10.47065/bits.v3i2.1004

Abstract

In 2019, at the end of December, there was an outbreak of a disease with an unknown cause in Wuhan, Hubei Province, China. The World Health Organization has named the outbreak of the disease as coronavirus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Covid-19. Covid-19 is a disease outbreak that has spread in various regions of Indonesia, such as in Riau, at PT. Nusantara V Plantation (PTPN V). So we need a way to increase awareness and vigilance, namely by presenting information using Data Mining in predicting the number of cases with thealgorithm Support Vector Regression (SVR). The prediction process is carried out using SVR by specifying the SVR and Kernel Linear parameters. The SVR algorithm can predict the number of Covid-19 patients in the next 30 days so that the Correlation Coefficienti (R) level is 85% and the Mean Square Error (MSE) value is 0.196. From the results of the experiment, there was a decrease in cases of Covid-19 patients at PT. Perkebunan Nusantara V in the next 30 days, with the acquisition of the best minimum sensitivity value of 0.09
Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir Sania Fitri Octavia; Mustakim Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.275 KB) | DOI: 10.47065/bits.v3i2.1005

Abstract

In the beginning of 2020 world was shocked because new virus spreaded, that is Coronavirus Disease 2019 (Covid-19). This virus spread quickly in almost country, including Indonesia. Covid-19 virus deployment started in various regions in Indonesia stay increasing everyday. this research has been done the region clustering that infected Covid-19 case in Indragiri Hilir district to inform to central government about Covid-19 handling. To do Clustering in this research used K-Means and Fuzzy C-Means Algorithm. After done some of test, it's obtained the ratio which was tested with Silhouette Index and Partition Coefficient, SI validity value of K-Means is 0,950 while PCI validity value of Fuzzy C-Means is 0,960. The results have been obtained shown that Fuzzy C-Means Method is the best Method to do Clustering Covid-19 data in Indragiri Hilir district Because the validity value is closed to 1 which is located in K=3.
Klasifikasi Text Dokumen Web Berbasis Supervised Learning Sebagai Pemodelan Aplikasi Pembelajaran Kebudayaan Melayu di Indonesia Mustakim Mustakim; Febi Nur Salisah; Suryani Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8499

Abstract

Indonesia, as the largest archipelagic country, is home to diverse cultures, including Malay culture in Riau Province. The website features numerous text documents, including articles, news, and personal documents, uploaded by members of the cultural community. This study aims to support the preservation of Malay culture through technology by implementing a digital learning system based on Machine Learning. Previous research has identified weaknesses in the application of intelligent systems and machine learning algorithms. This study tests five classification algorithms Random Forest, SVM, Naïve Bayes, KNN, and PNN to improve the system's accuracy and performance. The results show that Random Forest achieved the highest accuracy of 91.17%, followed by KNN at 88.23%, SVM and NBC at 82.35%, and PNN at 76.47%. The developed Digital Learning System (DLS) received positive feedback, with a User Acceptance Test (UAT) score of 86% and a 100% success rate in Blackbox testing, demonstrating stable performance across various devices. This research introduces a new innovation in Malay cultural preservation applications, utilizing Machine Learning algorithms to enhance both accuracy and functionality.
Co-Authors Adyah Widiarni Ahmad Harun Ahmad Rizky Andriawan Bella Oktavianti Dedi Pramana Delvi Nur Aini Dian Ayu Sabillah Dzul Asfi Warraihan Ena Tasia Febi Nur Salisah Inggih Permana Jeni Adhiva Kharisma, Oktaf Brillian M Afdal Muhammad Jalal Husain Muhammad Ridho Anugrah Muhammad Ridwan Nanda Nazira Nanda Try Luchia Pangeran Fadillah Pratama Qurotul A'yuniyah Qurotul A'yuniyah Rice Novita Said Thaufik Rizaldi Sania Fitri Octavia Siti Monalisa Suryani Suryani