<![CDATA[A8 Electronic & Furniture Piayu Store faces challenges in identifying frequently purchased product combinations and arranging products for customer convenience. This research aims to analyze consumer purchasing patterns and group consumers based on their habits using the Apriori algorithm and K-Means. The Apriori algorithm identifies frequent itemsets, such as a 55% likelihood of purchasing an AC after buying a fan and a tendency to purchase a mic with a speaker. K-Means clustering, with an optimal configuration of 4 clusters (Davis-Bouldin score: 0.754), categorizes 54 items in cluster 0, 18 in cluster 1, 20 in cluster 2, and 28 in cluster 3. These insights are recommended for optimizing stock management, tailoring promotions, and improving customer service. The study demonstrates the potential of transaction data analysis to support strategic decision-making and business growth.]]>
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