<![CDATA[This study aims to identify purchasing patterns of food menus in the campus cafeteria of STMIK Mulia Darma by applying the Apriori algorithm within the data mining framework. The background of this research is based on the increasing volume of transaction data that remains underutilized in supporting managerial decision-making. The Apriori algorithm is employed to uncover associations between items frequently purchased together by calculating their support and confidence values. A dataset of 20 daily digital transactions was used as the basis for analysis. The results revealed a single valid association rule that met the minimum threshold: Nasi Goreng,Teh Manis with a support value of 15% and a confidence value of 60%. This finding indicates a strong tendency in student consumption behavior, which can be leveraged for marketing strategies such as menu bundling and predictive inventory management. The study demonstrates that the Apriori algorithm can offer practical and strategic insights in the context of a campus cafeteria and holds potential for further development using larger datasets and more advanced analytical methods.]]>
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