<![CDATA[The management of student violation records in higher education requires a system that is fast, structured, well-documented, and accessible to authorized stakeholders. At Universitas Pamulang, the process of recording and monitoring student violations still has the potential to be carried out manually or through non-integrated mechanisms. This condition may lead to several problems, including delays in reporting, difficulty in tracing violation histories, limited supporting evidence, and weak coordination among parties involved in case handling. This study aims to design a web-based student violation monitoring system integrated with artificial intelligence to support the reporting, management, identification, and monitoring of student violations more effectively. The research method includes observation, interviews, literature study, system requirement analysis, and role-based system design. The proposed system provides several main features, including violation reporting, student data management, visual evidence upload, CCTV monitoring, AI-supported identification of suspected violators, violation history tracking, student response management, and monitoring dashboards for administrators and heads of study programs. The result of this study is a system design that supports the digitalization of student discipline monitoring through centralized data recording, faster reporting workflows, improved case traceability, and better documentation of supporting evidence. Artificial intelligence is positioned as a supporting component in the identification process, while final verification remains under the authority of human administrators to ensure fairness and accountability. Therefore, the proposed system is expected to improve the effectiveness, transparency, and accuracy of student violation management in higher education institutions.]]>
