<![CDATA[This research addresses student misconduct at SMAN 1 Rejotangan through the implementation of the Naïve Bayes Method in a web-based decision support system. The evaluation demonstrates a high accuracy level in classifying student misconduct. Functional tests confirm the successful management of attendance data and student violations, with the system providing accurate information and receiving positive feedback from users. In the context of Handling IDs, the Naïve Bayes method categorizes students into mild and moderate violations, with Handling 1 to Handling 5 encompassing these categories. Handling 6 and Handling 7 did not appear in the classification results for the second semester of 2023.The conclusion indicates that the implementation of the Naïve Bayes Method has successfully made a positive contribution by providing accurate information and supporting decision-making for the school authorities. Research recommendations include optimizing the Naïve Bayes algorithm, developing system features, actively involving relevant stakeholders, user training, and periodic system maintenance. These steps are expected to enhance the quality and effectiveness of the system in addressing the dynamics of student misconduct in the school environment.]]>
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