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Greenation International Journal of Engineering ScienceGreenation International Journal of Engineering Science

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.

This research successfully implemented the Apriori algorithm to identify frequent product combinations purchased together at Indomaret Perjuangan Bekasi.Based on the analysis, recommendations were made to strategically arrange products on shelves to enhance customer convenience and potentially increase sales.The study demonstrates the practical benefits of applying data mining techniques, specifically the Apriori algorithm, to optimize retail operations and improve customer shopping experiences.

Future research should expand the scope of this study by incorporating data from multiple Indomaret locations and a longer timeframe to achieve more generalized and robust findings regarding customer purchasing patterns. Furthermore, investigating the influence of external factors such as promotional activities, seasonal trends, and pricing strategies on product associations could provide a more comprehensive understanding of consumer behavior. Finally, exploring the integration of other data mining techniques, such as clustering or classification, alongside the Apriori algorithm could enable more refined customer segmentation and personalized product recommendations, ultimately leading to more effective store layout optimization and targeted marketing campaigns. These advancements will contribute to a deeper understanding of retail dynamics and empower retailers to make data-driven decisions that enhance customer satisfaction and maximize profitability.

  1. Vol. 2 No. 4 (2025): (GIJES) Greenation International Journal of Engineering Science (December 2024 -... research.e-greenation.org/GIJES/issue/view/33Vol 2 No 4 2025 GIJES Greenation International Journal of Engineering Science December 2024 research e greenation GIJES issue view 33
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