<![CDATA[This study analyzed the pattern of student violations at the Vocational Engineering Academy with a total of 5,901 recorded cases. The purpose of the study was to identify the distribution, temporal trends, and factors influencing violations, as well as formulate policy recommendations. The method used is descriptive-quantitative analysis with secondary data, including frequency analysis, categorization, correlation, and visualization using Python (Pandas, NumPy, Matplotlib, Seaborn). The results showed the dominance of verbal violations (85.5%) compared to written violations (14.5%), with the most categories being Other (43.6%), Assignments/Reports (23.9%), and Delays (21.6%). Temporal analysis revealed a pattern of student adaptation: violations were high in the early semester (especially equipment breakdowns and Other categories), decreased in the middle semester, but delays increased in the final semester due to academic load. Other findings showed low violations of Rules of Conduct (6.6%) and Equipment Damage (4.3%), reflecting relatively controlled non-academic discipline. The implications of the study highlight the need for a multi-tiered coaching system, improved task management, digital presence, and revision of breach categorization for data accuracy. Recommendations include the development of an integrated task management platform, academic mentoring programs, time management workshops, and data-driven policy evaluations. This research provides an empirical basis for institutions to design effective violation prevention strategies, especially through preventive approaches and real-time monitoring systems.]]>
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