Article Per Year (5 Year)
p-Index From 2020 - 2025
2.195
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Information Systems and Informatics Jurnal Pengabdian Masyarakat Bumi Raflesia DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Computer Science and Information Technology Jurnal Pengabdian Masyarakat Ilmu Komputer
Yadi Utama
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Mapping the Distribution of Covid-19 Information using a Web-based Information System Ibrahim, Ali; Okllilas, Ahmad Fali; Azhar, Iman Saladin B.; Utama, Yadi; Zahran, Ahmad Hafizh
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3773

Abstract

Based on information from several official government websites and national television media, at the beginning of November 2021, there was a decrease in the spread of the COVID-19 virus. At the end of November 2022, a new type of virus was discovered, namely SARS-CoV-2, also known as the Omicron variant, resulting in additional cases. Therefore, researchers conducted research with the aim of providing information about the spread of the virus with a mapping system down to the RT level, so that the public gets detailed and real-time information about the area based on mapping. The urgency of this research is that, with the results of this research, the public can take better care of and be able to provide self-awareness regarding health protocols. The public can have access to detailed information and mapping about the spread of the virus. This research adopts changes in the design science research methodology proposed by Hevner. The results of the research are an Information System for Mapping the Distribution of COVID-19 Cases and Their Danger Level with 5 calculation categories, namely class I with a range of 66–80 has very high danger level criteria, class II with a range of 51–65 has high danger level criteria, class III with a range of 36–50 has medium level criteria, class IV with a range of 36–50 has criteria for a low level of danger, and class V with a range of 5–20 has a very low level of danger.
Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat Tammam, Bimmo Fathin; Ibrahim, Ali; Indah, Dwi Rosa; Oklilas, Ahmad Fali; Utama, Yadi
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1365

Abstract

Effective management of taxpayer data is crucial for enhancing compliance and optimizing regional revenue. This study addresses the limited use of data-driven taxpayer segmentation in local Samsat institutions by applying K-Means Clustering to support targeted Customer Relationship Management (CRM) strategies. A dataset of 3,999 motor vehicle taxpayer records from September 2025 was processed through feature selection, scaling, and clustering. The analysis identified three distinct taxpayer groups based on payment timeliness, compliance consistency, and vehicle age. Cluster validity was confirmed using the Davies-Bouldin Index, yielding a value of -41.327 for k = 3, supported by ANOVA for statistical significance. The findings highlight how clustering can reveal taxpayer behavior patterns, guiding personalized services and compliance programs. This study's novelty lies in integrating clustering outcomes with practical CRM strategies for public agencies, offering a data-driven approach to improve taxpayer engagement and regional revenue. However, the study is limited by its focus on a single-period dataset and vehicle-related attributes.
Co-Authors Abdiansah, Abdiansah Affandi, Azhar Kholiq Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Al Farissi Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Alzaini, Akbar Anita Desiani Arhami, Muhammad Des Alwine Zayanti, Des Alwine Dhini Kurnia, Rizka Dwi Rosa Indah Endang Lestari Ermatita - Fathoni - Hardini Novianti Iman Saladin B. Azhar Irmeilyana Islamiansyah, Wira Kodri, Lay Mira Afrina Okllilas, Ahmad Fali Purwita Sari, Purwita Ramayanti, Indri Rezqe, Beriadi Agung Nur Ricy Firnando Rizka Dhini Kurnia Samsuryasi, Samsuryasi Sanjaya, M. Rudi Sasongko, Muhammad Aditya Savero, Muhammad Juan Tammam, Bimmo Fathin Thuraya, Zafira Wjaya, M.Aditya Yunus, Hedi Zahran, Ahmad Hafizh Zaini, Akbar Al Zhafiri, Muhammad Farisan