<![CDATA[Universities must optimize their information resources to enhance organizational performance and support strategic decision-making. However, academic data stored in multiple operational systems often remains fragmented and difficult to analyze comprehensively. This study aims to develop a data warehouse and apply data mining techniques to integrate and analyze academic data at the National University (UNAS), Jakarta. The data warehouse was designed using a star schema model, integrating academic records from various operational databases into a centralized repository. Mondrian and JPivot were utilized for multidimensional data presentation, while Classification-Based Association (CBA) and Association Rule techniques were applied to uncover hidden patterns within the data. The results show that the data warehouse significantly improves reporting efficiency, reducing processing time from one month to one day. Data mining analysis further revealed characteristic patterns among students in selecting specialization programs based on academic performance. These findings demonstrate that the integration of data warehousing and data mining supports more accurate reporting, informed decision-making, and data-driven academic planning.]]>
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