<![CDATA[The professional haircare industry in Indonesia has experienced rapid growth, requiring companies to manage inventory more accurately and in a data-driven manner. Inefficient inventory management may lead to overstock or stockout conditions, resulting in operational inefficiencies and lost sales opportunities. This study aims to apply the Eclat algorithm to identify sales transaction patterns of professional haircare products at Inaura, to uncover significant product association patterns, and to formulate inventory management recommendations based on the analysis results. The research employs a quantitative data mining approach using market basket analysis. The dataset consists of sales transaction records of professional haircare products at Inaura from January to December 2024. The Eclat algorithm is implemented with a minimum support threshold of 5% and a maximum itemset length of four items to generate frequent itemsets and meaningful association rules. The results indicate that the Eclat algorithm effectively and efficiently identifies sales transaction patterns that represent customer purchasing behavior. Products such as neutralizers, oxidising creams, and straightening systems exhibit the highest support values and form functional and complementary purchasing patterns. The extracted patterns can be utilized to support inventory planning, product prioritization, and data-driven bundling strategies. This study provides practical contributions to inventory optimization at Inaura and academic contributions by demonstrating the application of the Eclat algorithm in the underexplored domain of the professional haircare industry.]]>
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