<![CDATA[In a competitive business world, data-driven strategies are key to maintaining business continuity. Local brand shoe sales face challenges in managing increasing sales data, which is often only used for archives without providing added value in strategic decision making. This study aims to utilize data mining techniques, especially the Apriori algorithm, to analyze transaction patterns of local brand shoe sales. The Apriori algorithm was chosen because of its ability to find relevant association patterns from large transaction data. This study includes data pre-processing, pattern mining, and interpretation of results, with a focus on extracting relationships and linkages that can improve marketing strategies. The results of this study are expected to produce valuable information that supports decision making, while providing solutions to the lack of decision support systems in managing shoe sales data. Thus, this study contributes to the development of data-driven marketing strategies to improve the competitiveness of local shoe products.]]>
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