Analysis of Student Purchasing Patterns in a Campus Convenience Store Using Market Basket Analysis
DOI:
https://doi.org/10.37577/sainteks.v8i02.1144Keywords:
Apriori Algorithm, Market Basket Analysis, Convenience Store, Student Purchasing BehaviorAbstract
Understanding customer purchasing patterns is essential for retailers to develop effective merchandising and promotional strategies. However, conventional transaction analysis is often limited to identifying the frequency of individual product purchases without revealing relationships among products purchased within the same transaction. This study aims to identify purchasing patterns among university students by applying Market Basket Analysis (MBA) using the Apriori algorithm. Primary data were collected from 100 purchase receipts obtained from students who shopped at a convenience store located in a university area. The transaction data were cleaned, grouped into 18 product categories, and transformed into a binary transaction matrix prior to analysis. The Apriori algorithm was implemented using a minimum support of 3% and a minimum confidence of 60%, while the resulting association rules were evaluated using lift values greater than one. The results indicate that Dairy (45%) and Bakery (43%) were the most frequently purchased product categories. The most frequent two-item combination was Bakery–Dairy, with a support value of 21%. Furthermore, nine association rules satisfied the predefined thresholds, with the strongest rule indicating that purchases containing Mineral Water and Body Care were consistently associated with Bakery purchases (confidence = 100%; lift = 2.33). These findings demonstrate that Market Basket Analysis can provide valuable insights into customer purchasing behavior and support data-driven decisions related to product placement, merchandising, and promotional strategies in convenience stores located within university environments.
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