<![CDATA[This study analyzes purchasing patterns in minimarkets using the FP-Growth algorithm to optimize product layouts. One year of sales transaction data (103,181 transactions) from UNDIKNAS Mart were analyzed through data cleaning, transformation, and aggregation. The FP-Growth algorithm was applied with minimum support 5%, confidence 80%, and lift >1 thresholds. Results identified strong product associations, particularly between "Aqua 600 ml (Tanggung)" and various snacks, with confidence values of 81-93% and lift >5. Implementing these findings in product arrangement increased sales by 15-20% despite store accessibility limitations. Cross-validation using a decision tree model showed 81.67% accuracy. The findings demonstrate FP-Growth's effectiveness in small-scale transaction data analysis. The research provides practical contributions for retailers to boost sales through data-driven product layout optimization. A limitation is the single-location data scope, suggesting the need for broader subsequent studies. This study offers a data-based approach adoptable by small and medium retail businesses to enhance operational efficiency and profits. The research confirms that data mining techniques can significantly impact retail performance even in constrained environments, providing empirical evidence of FP-Growth's practical utility in real-world minimarket settings. The methodology and findings contribute to the growing literature on data mining applications in small-scale retail operations, offering replicable frameworks for similar business contexts.]]>
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