<![CDATA[The rapid growth of online marketplaces has generated massive transaction data, which is often underutilized in supporting marketing strategies, particularly in product bundling. This study aims to analyze digital product purchasing patterns using the FP-Growth algorithm to optimize bundling strategies in online marketplaces. The dataset used is the Online Retail dataset from the UCI Machine Learning Repository, which has undergone preprocessing, transformation, and analysis stages. The FP-Growth algorithm is applied to extract frequent itemsets and generate association rules based on support, confidence, and lift ratio metrics. The results indicate that FP-Growth effectively identifies relationships between frequently co-purchased products in an efficient manner. The generated association rules can serve as a foundation for developing bundling strategies and product recommendations. Therefore, the application of FP-Growth proves to be effective in enhancing the utilization of transaction data for business decision-making in online marketplaces.]]>
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