<![CDATA[Data Mining is an important technique in business analysis to discover hidden patterns in transaction data. This study compares the performance of two association rule algorithms, Apriori and FP-Growth, in identifying association patterns between products. This study aims to evaluate the efficiency of processing time and the quality of association rules generated by both algorithms in a retail context. The dataset used comes from transactions of Minimarket Adi Poday, covering 143,523 entries and 97,548 transactions from January to August 2024. The selection of this dataset is based on the relevance to the analysis of customer shopping patterns for marketing strategy optimization. Tests were conducted with a minimum support parameter of 0.01, and the results show that FP-Growth is superior in processing speed to Apriori, with an average execution time difference of 33.33% seconds faster on the same dataset. The implication of this research for minimarket owners is that the use of FP-Growth algorithm for purchasing pattern analysis can help in product arrangement and more effective promotion strategies. In addition, this research contributes to the field of Information Systems by demonstrating the effectiveness of FP-Growth in handling large-scale transaction data, as well as providing insight into the selection of algorithms suitable for retail business needs.]]>
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