Application of Data Mining with Apriori Algorithm on Furniture Sales to Support Business Intelligence

Abstract

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.