<![CDATA[Consumer purchasing pattern analysis is a strategic approach to understanding customer behavior and supporting data-driven decision-making. This research was conducted at AHHAS PT. Mitra Pinasthika Mustika Batam with the aim of identifying relationships between motorcycle service items that are frequently selected together by consumers. The method used is the Association Rules algorithm, specifically Apriori, with a quantitative approach applied to 4,026 transaction records from April to June 2025. The data were analyzed through preprocessing, one-hot encoding, and the application of the Apriori algorithm using parameters of minimum support of 5%, confidence of 30%, and lift > 1. The results showed 80 frequent itemsets and 38 valid association rules. The rule with the highest lift was Oil Chang}-Gear Set, indicating a strong correlation between the two services. Other patterns such as Spooring → Front Tire and Air Filter Balancing also showed significant associations. These findings can be utilized to design service bundling strategies, automatic recommendation systems, and optimize stock and workforce management. This study proves that the Apriori algorithm is effective in uncovering hidden patterns within transaction data, enhancing operational efficiency and service quality in the automotive sector.]]>
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