<![CDATA[Monitoring the attendance and performance of campus security personnel is essential to ensure operational effectiveness and service reliability. However, conventional monitoring approaches are often manual, fragmented, and prone to inaccuracies, limiting timely decision-making. This study proposes a stacking-based hybrid ensemble machine learning model for predicting security personnel attendance and classifying performance within the SIMSATPAM campus monitoring system at Institut Teknologi dan Bisnis Riau Pesisir. The proposed method integrates Random Forest, Support Vector Machine (SVM), and XGBoost as base learners, while Logistic Regression is employed as the meta-learner to improve predictive capability and classification robustness. To prevent overfitting and data leakage, the stacking architecture utilizes K-Fold Cross-Validation and Out-of-Fold (OOF) prediction mechanisms during meta-learner training. Experimental results demonstrate that the proposed stacking hybrid ensemble model outperforms individual classifiers across all evaluation metrics. The model achieved an accuracy of 94.27%, precision of 93.81%, recall of 93.45%, and F1-score of 93.62%, improving accuracy by 4.13% compared to Random Forest, 5.51% compared to SVM, and 2.24% compared to XGBoost. Furthermore, the proposed model produced stable multi-class classification performance for attendance prediction and personnel performance evaluation. Analysis indicates that attendance duration, shift schedule, and lateness frequency are the most influential variables affecting prediction outcomes. These findings confirm that the proposed stacking-based hybrid ensemble approach provides an effective intelligent decision-support system for automated campus security monitoring and personnel management in smart campus environments.]]>
