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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Pengabdian Tri Bhakti
Riadi Marta Dinata
Institut Sains dan Teknologi Nasional
Computer Science & IT Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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

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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.
Implementasi Smart Village: Peningkatan Tata Kelola dan Keamanan Lingkungan Melalui Sistem IoT dan Asisten AI di Desa Kedungwaringin Agus Sofwan; Riadi Marta Dinata; Kun Wardana; M. Febriansyah; Fivit Marwita; M. Ikrar; Niko Purnomo
Jurnal Pengabdian Tri Bhakti Vol 8 No 1 (2026): Jurnal Pengabdian Tri Bhakti
Publisher : LPPM Universitas Langlangbuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70825/jptb.v8i1.2402

Abstract

Village governments face dual challenges in the digital era: the demand for efficient public services and environmental security issues. In Kedungwaringin Village, these problems manifested as manual information services and the presence of unmonitored illegal dumping sites. This community service activity aimed to implement practical Smart Village solutions to improve village governance and environmental security through the application of appropriate technology. The method employed a quasi-experimental one-group pretest-posttest design involving education, live demonstrations, and hands-on training for the implementation of two key technologies: a solar-powered Internet of Things (IoT)-based environmental monitoring system, and a WhatsApp-based AI Assistant for information services. The results demonstrated a statistically significant increase in participant understanding (p < 0.05), with the 'Good' comprehension category rising from 8% to 32%. Furthermore, the activity successfully produced two functional, ready-to-use outputs that received a highly positive response from participants for being practical and relevant. Thus, the application of integrated IoT and AI technologies accessed via familiar platforms proved effective in accelerating digital transformation at the village level and can serve as a replicable pilot model
Co-Authors Agus Sofwan Ashari Abidin Bambang Satrio Fivit Marwita Kun Wardana M. Febriansyah M. Ikrar Niko Purnomo Risma Petrus Seno Lamsir