<![CDATA[This study explores the application of Data Mining using the Apriori algorithm in furniture sales to support Business Intelligence. The research process includes collecting weekly transaction data, forming frequent itemsets, analyzing association rules using metrics such as support, confidence, and lift, and integrating the results into business strategies. The findings indicate that tables, wardrobes, and bookshelves have the highest purchase rates at 100%, followed by cabinets at 83.33%, chairs at 91.67%, and sofas at 66.67%. Strongly associated itemsets, such as {Table, Bookshelf} and {Wardrobe, Cabinet}, provide valuable insights for business owners in designing marketing strategies, maintaining stock availability, and enhancing customer satisfaction. Utilizing the Apriori algorithm, this study successfully identifies significant purchasing patterns that can be used to drive sustainable business growth in the furniture industry.]]>
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