<![CDATA[Schools routinely collect academic, attendance, and character data, but their use remains limited to administrative recording. As a result, changes in student conditions are not always transformed into early risk signals, such as academic decline, repeated absenteeism, or accumulated character-related violations. The problem addressed in this study is how to evaluate an early detection model that can capture these three risk dimensions simultaneously using imbalanced and temporally ordered monthly longitudinal data. This study compares Decision Tree, Random Forest, and XGBoost for detecting Academic Risk, Attendance Risk, and Character Risk in the following month. The dataset consists of longitudinal student records from SMKS YUM Pesantren Teknologi Riau in 2025, covering 206 students across 12 months. The raw dataset contained 2,472 rows and was reduced to 2,266 valid rows after feature engineering and time-shifting target construction. Model evaluation used rolling-origin temporal evaluation, majority baseline, temporal persistence baseline, and imbalance handling strategies: no balancing, class_weight, and partial SMOTE on training data. Macro F1 was used as the primary metric because the class distribution was imbalanced. The best models were Random Forest with class_weight for Academic Risk, with Macro F1 of 0.5005 and High-Risk recall of 0.6185; Decision Tree with class_weight for Attendance Risk, with Macro F1 of 0.4586 and High-Risk recall of 0.5339; and Random Forest with class_weight for Character Risk, with Macro F1 of 0.4761 and High-Risk recall of 0.5698. These findings indicate that machine learning models can provide early High-Risk signals, but predictions should be verified by teachers or homeroom teachers.]]>
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