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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Abidin, Ashari
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Computer Science & IT

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Personalized Behavioral Analytics for GPS-Validated Attendance Systems Using K-Means Clustering and Individual-Baseline Anomaly Detection Abidin, Ashari; Dinata, Riadi Marta; Satrio, Bambang; Petrus, Risma; Lamsir, Seno
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.38881

Abstract

This study develops and evaluates a GPS-based attendance analytics framework integrating three complementary analytical layers for higher education environments. The proposed system combines spatial validation using Haversine-based geofencing, behavioral segmentation through K-Means clustering with multi-metric validation, and personalized anomaly detection employing individual-baseline Z-Score computation. Empirical evaluation utilized 4,300 attendance records from 13 lecturers at FSTT ISTN Jakarta over a 16-month period. K-Means clustering with K=3 achieved a Silhouette Score of 0.634 and a Davies-Bouldin Index of 0.621, identifying three behavioral segments: High Performers (30.8%), Moderate (38.5%), and Improvement Needed (30.8%). The personalized Z-Score method detected 19.9% more anomalies compared to population-based thresholds and reduced detection inequity across lecturer groups. Practically, the framework transforms passive attendance logging into a decision-support tool that enables differentiated monitoring, early behavioral change detection, and fairer evaluation policies. However, the study is limited by a relatively small sample size (13 lecturers) within a single institutional context, which may affect model generalizability. Broader validation across larger and multi-institutional datasets is recommended for future work.
Co-Authors Dinata, Riadi Marta Lamsir, Seno Petrus, Risma