<![CDATA[Water utilities continuously face challenges in identifying abnormal customer water consumption caused by pipe leakage, illegal connections, meter reading errors, and unusual consumption behavior. Conventional manual inspection requires considerable time and often delays corrective actions. This study presents the design and development of SIPADMA (Sistem Deteksi Anomali Pemakaian Air), a web-based information system developed using Software Engineering principles to automate anomaly detection and support operational decision making. The system was developed following the Waterfall Software Development Life Cycle (SDLC), beginning with requirement analysis, UML-based system modeling, implementation using Flask, and functional testing. SIPADMA integrates statistical anomaly detection using Z-Score combined with rule-based scoring and provides interactive dashboards, anomaly filtering, customer history visualization, and automatic report generation. Evaluation using 3,996 billing records representing 666 customers demonstrated that the system successfully identified 163 anomalous accounts requiring further inspection. The developed system improves operational efficiency by automating data analysis and prioritizing field verification, demonstrating the effectiveness of software engineering practices in developing intelligent decision-support systems for public utilities.]]>
Copyrights © 2026
