Implementation of the Apriori Algorithm for Product Arrangement in a Minimarket

Abstract

This research aims to implement the Apriori algorithm to analyze customer purchase patterns and provide recommendations for product placement in Indomaret Perjuangan Bekasi. Data mining techniques, specifically the Apriori algorithm, are applied to transaction data collected from the store between March 1 and April 1, 2025. The primary objective is to identify frequent itemsets and generate association rules that reveal which products are frequently purchased together, providing insights into customer purchasing behavior. The research begins by preprocessing the data into a format suitable for analysis using Weka, a data mining tool. The Apriori algorithm is then applied to uncover product combinations that occur frequently in transactions. Based on the association rules generated, the study recommends optimal product placement strategies to enhance customer convenience and increase cross-selling opportunities. The results show that certain product combinations, such as bread, jam, and milk, and instant noodles, eggs, and sauce, exhibit strong association, with a support of 0.25 and confidence of 0.75 for bread, jam, and milk, and support of 0.20 and confidence of 0.80 for instant noodles, eggs, and sauce. These findings suggest that placing these products in close proximity on the shelves can improve customer shopping experiences and increase sales. Additionally, combinations like coffee, sugar, and milk show a support of 0.15 and confidence of 0.85, indicating strong purchasing patterns. However, the study also acknowledges limitations, such as the small scope of the data and the focus on a single location. The study emphasizes that further research with larger datasets and multiple locations could provide more robust insights. This research demonstrates the practical application of data mining techniques in the retail sector, showing how the Apriori algorithm can optimize store operations and product placement. It provides valuable insights that can help retailers enhance customer satisfaction, streamline inventory management, and boost sales through data-driven decision-making.