<![CDATA[Disciplinary and ethical violations committed by police officers can affect organizational professionalism and reduce public trust in law enforcement institutions. The management of violation records at Polresta Gorontalo Kota remains largely administrative, making it difficult to identify violation patterns and support data-driven decision-making. This study aims to analyze disciplinary and ethical violation data using the Decision Tree C4.5 algorithm to develop a classification model and decision rules. The dataset consists of police disciplinary and ethical violation records collected between 2022 and 2026. The results indicate that the violation category attribute serves as the root node of the decision tree, with the highest Gain Ratio value of 0.694. The resulting model successfully classifies violations into three sanction levels—minor, moderate, and severe—while generating interpretable decision rules. Model evaluation using a confusion matrix achieved an accuracy of 70.8%. The findings demonstrate that the C4.5 algorithm is capable of identifying patterns between violation types and sanction levels, indicating its potential as a decision-support tool for managing disciplinary and ethical violations within the Indonesian National Police.]]>
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