<![CDATA[The increasing volume of sales transaction data in café businesses requires proper data management so that it can be utilized to support business decisionmaking. However, transaction data are often not optimally analyzed, causing customer purchasing patterns to remain unidentified. This study aims to identify customer purchasing patterns at Little Flock Cafe using the Apriori algorithm. The method employed is applied research with a quantitative approach. The data analyzed consist of one month of sales transaction records obtained through documentation of café sales data. The transaction data are represented in a boolean table and processed using the R software with the arulespackage. Minimum support and confidence parameters are applied to generate association rules among products. The results indicate several menu combinations that are frequently purchased together with lift values greater than one, indicating positive relationships among items. These purchasing patterns can be utilized to support menu arrangement strategies, promotional bundle development, and marketing effectiveness improvement. Therefore, the application of the Apriori algorithm is proven to provide useful insights for data-driven decision-making in café sales management.]]>
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