<![CDATA[This study aims to implement the Apriori algorithm to analyze sales transaction data and generate product recommendations at Toko Asyifa Serba 35.000. The research addresses the problem of underutilized transaction data, where sales records are only used for administrative purposes without further analysis to support marketing strategies and decision-making. The significance of this study lies in its contribution to enhancing data-driven decision-making in retail businesses, particularly in improving product promotion strategies, inventory management, and customer satisfaction. The research adopts an applied quantitative approach with an experimental design. Data were collected through observations, interviews, and documentation of sales transactions, and analyzed using data mining techniques, specifically the Apriori algorithm, to identify frequent itemsets and association rules based on support and confidence values. The results indicate that the implementation of the Apriori algorithm successfully uncovers patterns of consumer purchasing behavior, revealing combinations of products frequently bought together. The generated recommendations provide practical benefits for retail management, including more effective product bundling strategies, optimized shelf arrangement, targeted promotional campaigns, and improved inventory planning. These improvements can contribute to increased sales opportunities and better customer shopping experiences. These findings enable the development of a recommendation system that provides accurate and relevant product suggestions. The study concludes that the application of Apriori-based recommendation systems improves sales effectiveness, optimizes product placement, and enhances customer satisfaction. It is recommended that retail businesses adopt data mining techniques to maximize the value of transaction data and further develop integrated recommendation systems for better decision support.]]>
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