Analysis of Retail Product Sales Patterns Using the Apriori Algorithm at Toko Reika
DOI:
https://doi.org/10.22303/csrid-.17.1.2025.71-92Keywords:
Apriori Algorithm, Sales Patterns, Stock Management, Marketing Strategies, KDD (Knowledge Discovery in Database).Abstract
This research aims to analyze product purchasing patterns at Toko Reika by utilizing the Apriori algorithm as a data mining method. The analysis process is conducted through a series of stages in Knowledge Discovery in Database (KDD), which includes data selection, cleaning, transformation, analysis, and evaluation. The results of this study successfully identified 36 association rules from the analyzed transactions, illustrating various combinations of related products. One of the most striking findings is the rule with the highest lift value, which is the combination of Basic Needs, Food Supplements, and Food Ingredients, with a lift value of 8.7. This indicates that these three products have a very strong correlation in consumer transactions. Additionally, the combination of Snacks, Basic Needs, and Food Ingredients also stands out, with a confidence value reaching 76%. This suggests that consumers who purchase one product from this combination are highly likely to purchase the other products as well. The analysis also reveals significant purchasing patterns within certain categories, such as Skincare, Food Supplements, and Bathing Supplies, which show high lift values and meaningful relationships between products in a single transaction. The insights gained from this research can be utilized to design data-driven marketing strategies, such as bundling promotions, product arrangement, and more effective stock management. It is hoped that these findings can help retail stores improve operational efficiency, maximize sales, and provide a better shopping experience for consumers.
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