<![CDATA[Sri Indah Lestari is a company engaged in the sales of interior accessories and architectural hardware. The company faces challenges in understanding consumer purchasing patterns and lacks accurate data to design effective promotional strategies. This study aims to apply the Apriori algorithm to analyze transaction data and identify frequent itemsets that can be used to develop targeted promotions. The research uses a descriptive quantitative method with a data mining approach. Sales transaction data from September to December 2024 were processed using Tanagra software. The analysis applied a minimum support value of 30% to identify product combinations frequently purchased together. The results show that Cylinder Dekkson Cyl, Door Closer Dekkson, and Hinge Dekkson are the most frequently purchased items. Several strong association rules were also identified based on support and confidence values. These findings can help the company design more effective product bundles and improve promotional strategies. The Apriori algorithm has proven to be a useful tool in generating data-driven insights for business decision-making at PT. Sri Indah Lestari. Keywords: Data Mining, Apriori Algorithm, Purchase Pattern, Promotion Strategy, Tanagra]]>
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