Market basket analysis of fresh fruit retail using the apriori algorithm: identifying purchasing patterns and popular products - Syoba Ismaunun; Selsa Trikartika; Sri Rezeki Candra Nursari; Muhammad Abiyu Muflih Az Arianto (Institute of Computer Science (IOCS), 2026)
The fruit retail sector continues to grow; however, most market basket analysis studies focus on non-perishable products, while fresh fruit retail remains underexplored despite its perishable and demand-volatile characteristics. This lack of empirical evidence often leads to inefficient stock management, inventory spoilage, and frequent stock-outs. This study addresses this research gap by applying the Apriori algorithm to analyze purchasing patterns and identify popular products in fresh fruit retail. The dataset consists of 50 sales transactions involving 25 fruit items collected from a single retail store. A minimum support threshold of 30% and a minimum confidence threshold of 60% were used to generate association rules. The results show that Citra Guava and Matoa are the most popular fruits, each with a support value of 62%. Several strong association rules, including Citra Guava–Matoa and Deli Guava–Matoa, exhibit confidence values above 80% and lift values greater than 1. These findings indicate that purchasing patterns in fresh fruit retail are relatively simple and concentrated. This study contributes by extending the application of market basket analysis to perishable product contexts and providing data-driven insights to support inventory planning and promotional strategies in fruit retail