<![CDATA[Toko XYZ, a retail company in Denpasar, faces challenges in managing its product stock due to the lack of utilization of consumer purchasing patterns in product placement and inventory management. As a result, products that are frequently bought together by customers often run out of stock, leading to potential sales declines. To address this issue, a purchasing pattern analysis was conducted using the association rules method with the ECLAT algorithm. The analysis was performed using Visual Studio Code with Python programming language to identify frequent 1-itemsets and frequent 2-itemsets, which represent products frequently purchased individually or in combination by customers. The analysis results show that lower minimum support values result in more frequent item sets being identified while increasing the minimum confidence, which narrows the number of generated association rules. At a minimum support of 0.05, 2463 rules were identified, whereas at a minimum support of 0.20, the number of rules decreased to 117. Higher minimum confidence, set at 60%, produced more relevant and reliable rules, demonstrating the potential of the ECLAT algorithm in aiding inventory management and optimizing product placement at Toko XYZ.]]>
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