Published: 2026-06-12
Implementation of the Apriori Algorithm for Product Recommendation Analysis at Asyifa Serba 35.000 Retail Store in Kisaran
DOI: 10.35870/ijmsit.v6i1.7289
Ardiansyah Putra Tambunan, Adi Prijuna Lubis, Parini
- Ardiansyah Putra Tambunan: Universitas Royal
- Adi Prijuna Lubis: Universitas Royal
- Parini: Universitas Royal
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Abstract
This study aims to implement the Apriori algorithm to analyze sales transaction data and generate product recommendations at Toko Asyifa Serba 35.000. The research addresses the problem of underutilized transaction data, where sales records are only used for administrative purposes without further analysis to support marketing strategies and decision-making. The significance of this study lies in its contribution to enhancing data-driven decision-making in retail businesses, particularly in improving product promotion strategies, inventory management, and customer satisfaction. The research adopts an applied quantitative approach with an experimental design. Data were collected through observations, interviews, and documentation of sales transactions, and analyzed using data mining techniques, specifically the Apriori algorithm, to identify frequent itemsets and association rules based on support and confidence values. The results indicate that the implementation of the Apriori algorithm successfully uncovers patterns of consumer purchasing behavior, revealing combinations of products frequently bought together. The generated recommendations provide practical benefits for retail management, including more effective product bundling strategies, optimized shelf arrangement, targeted promotional campaigns, and improved inventory planning. These improvements can contribute to increased sales opportunities and better customer shopping experiences. These findings enable the development of a recommendation system that provides accurate and relevant product suggestions. The study concludes that the application of Apriori-based recommendation systems improves sales effectiveness, optimizes product placement, and enhances customer satisfaction. It is recommended that retail businesses adopt data mining techniques to maximize the value of transaction data and further develop integrated recommendation systems for better decision support.
Keywords
Data Mining; Apriori Algorithm; Product Recommendation; Sales Transaction; Retail Strategy
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Article Information
This article has been peer-reviewed and published in the International Journal of Management Science and Information Technology. The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 6 No. 1 (2026)
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Section: Articles
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Published: 2026-06-12
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License: CC BY 4.0
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Copyright: © 2026 Authors
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DOI: 10.35870/ijmsit.v6i1.7289
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Ardiansyah Putra Tambunan, Universitas Royal
Information Systems Study Program, Faculty of Computer Science, Universitas Royal, Asahan Regency, North Sumatra, Indonesia
Adi Prijuna Lubis, Universitas Royal
Information Systems Study Program, Faculty of Computer Science, Universitas Royal, Asahan Regency, North Sumatra, Indonesia
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