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Hardiansyah Ramadhan
Universitas Lancang Kuning
Computer Science & IT

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K-Means Clustering in Determining the Category of Stock Items In Angkasa Mart Roki Hardianto; Hardiansyah Ramadhan; Eddissyah Putra Pane; Yogi Yunefri
Knowbase : International Journal of Knowledge in Database Vol 2, No 1 (2022): June 2022
Publisher : Institut Agama Islam Negeri (IAIN) Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/ijokid.v2i1.5411

Abstract

A good and accurate stock management system is crucial in any institution or organization that conducts product buying and selling transactions. This is done to improve stock efficiency, reduce storage costs, be more effective, and meet customer expectations. This research produced three groups of the most desirable products for large stock quantities, moderate stock quantities for products of interest, and low stock quantities for products that were less or not in demand. Data was processed using the clustering method, specifically the K-Means method, which was based on historical sales data that includes the product code, number of transactions, and average sales. This study was carried out using web-based calculations. Testing with the rapid miner application. The research yielded three members of the product group for large stock, 238 large stock, 273 medium stock, and 25 low stock.
K-Means Clustering in Determining the Category of Stock Items In Angkasa Mart Roki Hardianto; Hardiansyah Ramadhan; Eddissyah Putra Pane; Yogi Yunefri
Knowbase : International Journal of Knowledge in Database Vol 2, No 1 (2022): June 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (906.765 KB) | DOI: 10.30983/ijokid.v2i1.5411

Abstract

A good and accurate stock management system is crucial in any institution or organization that conducts product buying and selling transactions. This is done to improve stock efficiency, reduce storage costs, be more effective, and meet customer expectations. This research produced three groups of the most desirable products for large stock quantities, moderate stock quantities for products of interest, and low stock quantities for products that were less or not in demand. Data was processed using the clustering method, specifically the K-Means method, which was based on historical sales data that includes the product code, number of transactions, and average sales. This study was carried out using web-based calculations. Testing with the rapid miner application. The research yielded three members of the product group for large stock, 238 large stock, 273 medium stock, and 25 low stock.
Co-Authors Eddissyah Putra Pane Roki Hardianto Yogi Yunefri, Yogi