Journal of Risk & Control

Market Basket Analysis Using Apriori Algorithm: Identifying Consumer Purchase Patterns for Strategic Business Decisions

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• Abstract

   

  Market Basket Analysis (MBA) is a data mining technique used to discover association rules from transaction data, supporting the development of effective marketing strategies. This study applies the Apriori algorithm to transaction data from Maetala Café in Tarakan City during the period of October 2024 to January 2025. The Apriori algorithm efficiently identifies frequent itemsets and determines potential associations between purchased items based on minimum support and confidence thresholds. The data were processed using RStudio with the apriori algorithm, involving stages of data preprocessing, rule generation, and evaluation using support, confidence, and lift metrics. The results reveal that the strongest association rule is between Chicken Rice Salad and Mineral Water, with a support value of 4.11% and confidence of 68.13%, indicating a strong and consistent purchasing pattern. These findings suggest that consumers tend to purchase main dishes alongside mineral water as a complementary item. The identified association rules provide valuable insights for café managers in implementing cross-selling, designing bundled promotions, and optimizing product recommendations to increase sales performance.

   

  Keywords: Algoritma Apriori, Data Mining, Market Basket Analysis, Assosiation Rules, R Studio.