<![CDATA[Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.]]>
