Integration of Voting-Based Statistical Ensembles and Rule Mining for Anomaly Detection in Microsatellite Power System - Rizki Permala; Imas Sukaesih Sitanggang; Hendra Rahmawan; Wahyudi Hasbi (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2026)
This study aims to detect anomalies, types of anomalies, and identify attributes that contribute to the cause of anomalies in the LAPAN-A2 microsatellite power system. Anomaly detection is cumulative-based, where the first year's dataset is added to the datasets of subsequent years for eight years. Anomaly detection uses a voting-based statistical ensemble (VBSE) approach and a combination of Apriori-Close to find associations and reduce rules. The VBSE obtained an average recall value of 86.49 and an average F1-score of 70.49. The F1-score value of VBSE increased by 4.2-fold compared with IForest (16.95), by 12.6-fold compared with ECOD (5.57), and by 5.6-fold compared with LOF (12.65). VBSE showed an increase in performance as the dataset complexity increased (DS1 → DS8), whereas IForest, LOF, and ECOD tended to decrease. The combination of metrics minSupp. 0.02%, minConf. 0.9, and lift is proven to be effective in capturing rare, reliable anomalies and significant association relationships. A strong correlation was observed between batteries (VBatt1–VBatt3) and a causal relationship between U_UMPB and VBatt. The types of anomalies detected included single, contextual, and correlation anomalies.