<![CDATA[Advances in information technology have encouraged educational institutions to generate large amounts of student academic data, such as grades, attendance, and learning activities. However, in practice, this data is still mostly used for administrative purposes and has not been optimally utilized to improve the performance of the learning system. This study aims to analyze student academic performance patterns and optimize the performance of the academic data-based learning system at the senior high school (SMA) level from an industrial engineering perspective. This study uses a descriptive quantitative approach with a case study method at SMAS Al Azhar Medan. The research data consists of student academic data as secondary data and student perception data as primary data obtained through questionnaires. Data analysis techniques include descriptive statistical analysis, simple correlation analysis, and analysis of the gap between actual conditions and learning performance standards. The results show that attendance and assignment scores have a positive relationship with student learning outcomes. In addition, there is still a gap between actual learning performance and the ideal conditions set by the school. Based on these analysis results, this study produces recommendations for optimizing the learning system oriented towards process improvement and data-based learning decision making. This study is expected to be an initial reference in the development of an academic data-based learning system at the high school level.]]>
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