<![CDATA[In an increasingly digital era and increasingly fierce business competition, businesses must become more innovative to find out how consumers usually buy products. This can be achieved by collecting data from every transaction in which a consumer purchases a product. This research analyzes exploration of product purchase transaction data using the apriori association method in data mining, with primary data obtained from PT employees. Sinar Sosro Tasikmalaya during February 2024. Limitations of this research include a limited data period so the sample size used is small, which can affect the generalization of the results. However, the choice of parameter values of support of 0.1 and confidence of 1 helps to compensate for the limited sample size while still providing relevant and high-quality rules. The results of this research produce 3 association rules that can be used to support decisions, such as more efficient product placement and sales bundles. Where, the results of associations that have a strong product connection can be bundled with products that are less popular, and products that are often purchased together can be placed closer to the position of the product in the warehouse to make it easier to pick up the product.]]>
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