<![CDATA[IB Boutique is a retail company dedicated to fulfilling diverse clothing needs through both online and offline sales channels. The company generates substantial transaction data, which, when stored in a database, can be transformed into valuable insights using data mining techniques. However, IB Boutique faces challenges in inventory management, necessitating the need for effective analysis of transaction data to uncover patterns that can contribute to increased revenue. This study employs the Apriori algorithm, a widely recognized method for processing transaction data, to analyze 400 transaction records collected from IB Boutique. The patterns identified are based on support and confidence levels, which are critical metrics in association rule mining. To derive accurate association rules, the study utilizes Tanagra software, setting a minimum support threshold of 30% and a minimum confidence level of 65%. The analysis reveals that blouses and hijabs emerge as the best-selling products within the boutique's inventory. By understanding these best-selling items and the underlying customer buying patterns, IB Boutique can implement more effective marketing strategies aimed at promoting other products. This can be achieved through a comprehensive analysis of the profitability of the best-selling items, leading to the design of targeted marketing campaigns and the strategic arrangement of product layouts to enhance visibility and drive sales.]]>
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