<![CDATA[The development of information technology has led to a significant increase in the volume of sales transaction data stored in business information systems. Such data possess substantial potential to generate strategic insights when properly analyzed. However, in many small and medium-sized enterprises (SMEs), transaction data have not yet been optimally utilized. This study aims to apply association analysis using the Apriori algorithm to sales transaction data of woven products at Sientong Tenun in order to identify consumer purchasing patterns based on support and confidence values. The research adopts a quantitative approach employing data mining methods on sales transaction data that have undergone a data preprocessing stage. The final dataset used in this study consists of 120 sales transactions. The parameters applied in the analysis include a minimum support threshold of 20% and a minimum confidence threshold of 60%. The results indicate that all main products meet the criteria for frequent 1-itemsets, with Woven Fabric and Shawl exhibiting the highest support values, at 65.00% and 58.33%, respectively. The strongest association rule identified is Woven Fabric → Shawl with a confidence value of 70.51%, followed by Woven Fabric and Shawl → Woven Sarong with a confidence value of 63.64%. These findings demonstrate a significant purchasing relationship among woven products. The results of this study can be utilized by business practitioners to support marketing strategies, sales bundle development, product arrangement, and data-driven inventory management. Furthermore, this research contributes academically to the application of the Apriori algorithm within the culturally based creative industry sector.]]>
