Analysis of Student Purchasing Patterns with Market Basket Analysis (MBA) Using the Apriori Algorithm in the FT UNTIRTA Canteen - Patricia Pingkan Kumenap; Stella Caroline Roma Ito; Muhammad Fabian Reinhard Delano; Syukron Faiz; Aulia Ikhsan; Miftahus Sholihin; Atia Sonda (Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa, 2026)
Sales activities have an important role in business sustainability, especially in the food and beverage sector, where understanding consumer purchasing behavior and effective inventory management are crucial. This research aims to analyze student purchasing patterns in the canteen of the Faculty of Engineering, Sultan Ageng Tirtayasa University using the Market Basket Analysis method based on the Apriori algorithm. The data used is primary transaction data using a purposive sampling technique of 322 valid transactions. Analysis was carried out using association rule mining with minimum support and confidence parameters to identify relationships between items. The results show that the strongest association rule involves the combination of lime leaf rice, jumbo iced tea, and grilled chicken with a support value of 0.047, confidence 0.75, and lift 2.95. Apart from that, the rule also found was lime leaf rice grilled chicken with support 0.096, confidence 0.66, and lift 2.59. Several other rules have high confidence but low lift due to the dominance of white rice items. These findings indicate that students tend to buy a combination of main food, side dishes and drinks in one transaction. The Apriori algorithm has been proven to be able to identify significant purchasing patterns and can support product structuring, promotion and inventory management strategies.