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Suhardiansyah Suhardiansyah
University Pembangunan Panca Budi, Medan, Indonesia
Automotive Engineering Computer Science & IT

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Journal : "Journal of Data Science

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Optimization of Kaayana Store Inventory through Transaction Pattern Analysis Using the Apriori Algorithm Suhardiansyah Suhardiansyah; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6398

Abstract

This study aims to optimize inventory management at Kaayana Store by analyzing sales transaction patterns using the Apriori algorithm. The transaction data collected shows that products with the codes ACC (accessories) and BJU (clothing) dominate purchases, accounting for 71.4% of total transactions. The analysis results identify a strong relationship between these products, which are frequently purchased together by consumers. Based on these findings, Kaayana Store needs to ensure the availability of ACC and BJU stocks to meet high demand, avoid stockouts, and improve operational efficiency. Proposed inventory management strategies, such as more precise product placement and bundling promotions, are expected to enhance customer satisfaction and support the sustainability of Kaayana Store's business
Co-Authors Muhammad Iqbal