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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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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.
Hybrid Random Forest Regression and Ant Colony Optimization for Delivery Route Optimization Aurelia, Reni; Rahman, Abdul
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.v7i4.1376

Abstract

The transportation of goods in Indonesian cities is increasingly challenged by urbanization, congestion, diverse road characteristics, and environmental factors, reducing the effectiveness of conventional distance-based routing. This study enhances delivery route optimization by integrating travel-time prediction using Random Forest Regression (RFR) with a metaheuristic routing process using Ant Colony Optimization (ACO). Using OpenStreetMap (OSM) data for Palembang, experiments were conducted on five simulated customer locations in Zone 1. Road attributes such as segment length, road type, and estimated speed were used to train the RFR model, whose predicted travel times served as dynamic costs in the ACO heuristic. The RFR model achieved high predictive accuracy (R² = 0.98; MSE = 8.81), and the ACO-based optimization produced an efficient route of 29.58 km with a total travel time of 148 minutes. However, the experiment is limited to a single zone, a small number of customers, and the removal of real traffic variables—where all actual speed variations, congestion levels, and time-dependent traffic conditions were simplified or omitted, causing the model to rely solely on static road attributes. Future work will incorporate real-time traffic data, expand testing to multiple zones, and use larger datasets to improve scalability and operational applicability.
Optimized K-Means Clustering for Web Server Anomaly Detection Using Elbow Method and Security-Rule Enhancements Trianto, Rahmawan Bagus; Muin, Muhammad Abdul; Vikasari, Cahya
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.v7i4.1391

Abstract

Anomaly detection in web server environments is essential for identifying early indicators of cyberattacks that arise from abnormal request behaviors. Traditional signature-based mechanisms often fail to detect emerging or obfuscated threats, requiring more adaptive analytical approaches. This study proposes an optimized anomaly detection model using K-Means clustering enhanced with engineered security-rule features and the Elbow Method. Two datasets were used: a small dataset of 3,399 log entries from one VPS and a large dataset of 223,554 entries collected from three VPS nodes, all sourced from local production servers of the Department of Computer and Business, Politeknik Negeri Cilacap. The preprocessing pipeline includes timestamp normalization, removal of non-informative static resources, numerical feature scaling, and TF-IDF encoding of URL paths. Domain-driven security features entropy scores, encoded-payload indicators, abnormal status-code ratios, and request-rate deviations were integrated to improve anomaly separability. Experiments across five model configurations show that combining larger datasets with rule-based features significantly enhances clustering performance, achieving a Silhouette Score of 0.9136 and a Davies–Bouldin Index of 0.4712. The results validate the effectiveness of incorporating security-rule engineering with unsupervised learning to support early-warning threat detection in web server environments.

Page 66 of 66 | Total Record : 653

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