<![CDATA[This study aims to explore sales transaction data of spare parts at OTOXPERT Batam usingthe Apriori algorithm to identify association patterns among products. The main problemaddressed is that transaction data, although available in large quantities, has not beenoptimally utilized to uncover relationships between spare parts, so the potential use of thesepatterns to support cross-selling activities and stock management has not been fullyrealized. The data used in this study were sales transactions from January 1 to March 31,2025. The research method includes data preprocessing, transformation of transaction datainto basket form, descriptive analysis, and the application of the Apriori algorithm usingsupport, confidence, and lift parameters. The results show that the Apriori algorithm is ableto discover frequent itemsets and association rules that describe the tendency of spareparts to be purchased together. These patterns provide meaningful information aboutcustomer purchasing behavior and can be used as supporting information in decisionmaking related to sales strategies and inventory management. Therefore, this studydemonstrates that the Apriori algorithm is effective in transforming transaction data intovaluable information for business analysis at OTOXPERT Batam.]]>
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