<![CDATA[Aba Mart is a convenience store that provides a wide range of daily necessities. One of the challenges faced by Aba Mart is the uncertainty in determining the optimal timing for product promotions. To address this issue, this study utilizes sales transaction data obtained from the store’s Point of Sale (POS) system, totaling 12,887 transactions recorded from March to August 2024. The dataset includes attributes such as date and product name, which were processed through attribute selection, categorization into 33 product types, conversion of dates to days, and transformation into boolean format for analysis. The study applies the Association Rule Mining (ARM) technique using the Frequent Pattern Growth (FP-Growth) algorithm to identify the relationship between the time of purchase and the types of products bought. The results demonstrate that the FP-Growth algorithm successfully identified patterns of association. By testing with minimum support values of 2%, 3%, and 4%, and a minimum confidence of 10%, the analysis produced 15 association rules in March, 11 in April, 14 in May, 13 in June, 11 in July, and 13 in August 2024. These rules have been used as a foundation for formulating more effective and targeted promotional strategies for Aba Mart.]]>
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