<![CDATA[Nasi Uduk Mama Ipan is a micro-enterprise that conducts sales through both offline and online platforms. However, only online transaction data is available in analyzable form, while the owner lacks the knowledge to process it. This situation highlights the urgency of leveraging data mining techniques to uncover hidden patterns that can inform effective promotional strategies. This study aims to apply association rule mining using Apriori and FP-Growth algorithms, enhanced through metaheuristic-based hyperparameter tuning, to extract meaningful product bundling insights from transactional data. The research begins with data preprocessing, which involves eliminating irrelevant columns and transforming transactional records into a binary format. Four metaheuristic algorithms—Genetic Algorithm, ACO, PSO, and SA—are employed to determine optimal support and confidence values for both Apriori and FP-Growth. The modeling phase is conducted using Python with the mlxtend.frequent_patterns library, with rules filtered using a lift ratio threshold above 1. Results show that both Apriori and FP-Growth algorithms produce identical bundling recommendations using parameters derived from the Genetic Algorithm. Apriori performs faster, while FP-Growth is more memory-efficient. This study demonstrates that combining association rule mining with metaheuristic optimization can effectively support MSMEs in making data-driven marketing decisions.]]>
