K-Means Clustering in Determining the Category of Stock Items In Angkasa Mart

Authors

  • Roki Hardianto Universitas Lancang Kuning, Indonesia
  • Hardiansyah Ramadhan Universitas Lancang Kuning, Indonesia
  • Eddissyah Putra Pane Universitas Lancang Kuning, Indonesia
  • Yogi Yunefri Universitas Lancang Kuning, Indonesia

DOI:

https://doi.org/10.30983/ijokid.v2i1.5411

Keywords:

Clustering, K-Means, Web-Based

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.

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Published

2022-06-30

How to Cite

Hardianto, R., Ramadhan, H., Putra Pane, E., & Yunefri, Y. (2022). K-Means Clustering in Determining the Category of Stock Items In Angkasa Mart. Knowbase : International Journal of Knowledge in Database, 2(1), 30–45. https://doi.org/10.30983/ijokid.v2i1.5411

Issue

Vol. 2 No. 1 (2022): June 2022

Section

Articles

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Copyright (c) 2022 Roki Hardianto, Hardiansyah Ramadhan, Eddissyah Putra Pane, Yogi Yunefri

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