<![CDATA[Small and medium-scale hardware stores generally still rely on intuition when determining product layout without systematically considering customer purchasing patterns, causing cross-selling opportunities to remain underutilized. This study aims to analyze product association patterns in hardware store transaction data using the Apriori algorithm as the basis for a data-driven product placement strategy. The dataset consists of 90 transaction rows representing 30 unique transactions involving 9 product types. Research stages include data cleaning, one-hot encoding transformation, and application of the Apriori algorithm with a minimum support of 15% and minimum confidence of 40%. The analysis identified 14 frequent itemsets and 10 association rules, all with lift values above 1.0, indicating positive associations. The strongest rules were Wall Paint → Brush with a confidence of 77.78% and lift of 1.46, and Sand → Brick with a confidence of 50.00% and lift of 1.50. These findings provide an empirical foundation for shelf zone arrangement recommendations, product bundling packages, and stock management prioritization in hardware stores. The contribution of this research is to provide a data-driven analytical framework that can be directly adopted by small and medium-scale hardware store managers without requiring complex technological infrastructure, while also extending the application of the Apriori algorithm to the hardware store domain with a specific focus on physical product placement strategies.]]>
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