<![CDATA[This study aims to analyze consumer purchasing patterns and compare the performance of the Apriori and FP-Growth algorithms in association rule mining using transaction data of Avian paint products at PT. XYZ. The data used in this research are secondary sales transaction data from January to March 2025. The research method applies association rule mining based on the Knowledge Discovery in Databases (KDD) framework, including data cleaning, data selection, data transformation, data mining, and pattern evaluation stages. Evaluation parameters consist of support, confidence, lift ratio, number of association rules, and execution time. The results indicate that both algorithms generate the same number of association rules with an average confidence value of 96.3% and an average lift ratio of 4.82, indicating strong positive correlations among products. However, FP-Growth demonstrates a slightly faster execution time compared to Apriori, suggesting better computational efficiency. These findings imply that FP-Growth is more efficient in processing transaction data, while Apriori remains advantageous in terms of result interpretability. The extracted purchasing patterns can support data-driven marketing strategies, such as cross-selling, product bundling, and inventory management]]>
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