<![CDATA[The rapid growth of the cosmetics industry has created intense competition among retail companies, including PT. Ouzen Anugerah Indonesia, which faces challenges in understanding consumer purchasing behavior due to the lack of systematic data analysis. This study aims to analyze consumer purchasing patterns and identify frequently purchased product combinations using the Apriori algorithm as a data mining technique. The research employed a descriptive quantitative approach based on 5,000 sales transactions recorded between January and December 2024, consisting of transaction IDs, dates, and purchased product lists. Through the Apriori algorithm, the study generated association rules that reveal relationships among products, represented by support and confidence values. The results show that several cosmetic items exhibit strong associative relationships, indicating consistent consumer purchasing tendencies that can be utilized for cross-selling strategies, promotional bundling, and inventory optimization. These findings highlight the effectiveness of data mining in transforming raw transaction data into actionable insights that support data-driven business decision-making. The study contributes theoretically by reinforcing the application of association rule learning in medium-scale retail contexts and practically by providing a framework for developing marketing and operational strategies based on data analysis. The research also suggests that future studies integrate broader datasets and additional variables, such as consumer demographics and temporal factors, to enhance analytical depth and model generalization.]]>
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