<![CDATA[Islamic boarding schools (pesantren) are Islamic educational institutions that emphasize discipline in the daily lives of their students. However, the manual recording and evaluation of violations makes it difficult for administrators to conduct systematic analysis. This study aims to apply data mining methods, specifically the Decision Tree algorithm, to analyze student violation data for effective classification. Violation data is grouped into three categories: minor, moderate, and severe violations. A classification model is built through entropy and information gain calculations to determine the best attributes to serve as the initial nodes in the decision tree structure. The attributes used in the model include violation points, violation types, and dormitories. The results show that the Decision Tree algorithm is able to identify violation categories with excellent performance. In the minor category, the model achieved a precision of 0.99, a recall of 1.00, and an f1-score of 1.00. For the moderate category, the model achieved a precision of 0.94, a recall of 1.00, and an f1-score of 0.97, indicating that the model is able to recognize violations with a high level of accuracy and consistency. Meanwhile, in the severe category, the model demonstrated perfect precision of 1.00, recall of 0.87, and f1-score of 0.93. Overall, the model achieved 98% accuracy based on the confusion matrix evaluation, indicating that most of the data was correctly classified. The decision tree visualization also showed that the violation point, type of violation, and dormitory location were the main factors in the classification process. These results demonstrate that a data mining approach can be used to support data-driven decision-making in student guidance.]]>
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