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All Journal AL-ULUM: JURNAL SAINS DAN TEKNOLOGI
Siti Fathimah
Department of Information System, STMIK Banjarbaru, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Energy

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BEHAVIOR-BASED ANOMALY DETECTION IN ONLINE ATTENDANCE SYSTEMS USING A GENETIC ALGORITHM–OPTIMIZED ISOLATION FOREST Wahyudi Ariannor; Budi Susarianto; Siti Fathimah; Siti Abidah
AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Vol 12 No 2 (2026)
Publisher : UPT Publication and Journal Management, Islamic University of Kalimantan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jst.v12i2.23234

Abstract

Digital attendance systems are vulnerable to anomalous behaviors that may reduce attendance integrity and reliability. This study proposes an anomaly detection approach using Isolation Forest optimized with a Genetic Algorithm to improve anomaly detection performance in digital attendance environments. The proposed model utilizes behavioral features, including location, device consistency, network connectivity, and working duration. Experimental evaluation using Stratified 5-Fold Cross Validation demonstrates that the GA Hybrid approach significantly improves model performance, increasing the F1-score from 0.2934 to 0.6004 and recall from 0.1791 to 0.4345 while maintaining high precision. Feature analysis further reveals that location-based attributes are the most influential indicators in detecting anomalous attendance behavior. The results indicate that the proposed optimization approach can improve anomaly detection effectiveness while maintaining stable model performance.
Co-Authors Budi Susarianto Siti Abidah Wahyudi Ariannor