Implementation of K-Means Clustering for Inventory Item Classification in Inventory Management

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Authors

  • Febry Yusmita Universitas Dharmawangsa

Keywords:

K-Means Clustering; Data Mining; Inventory Management; RapidMiner; Inventory Classification;

Abstract

This study aims to implement the K-Means Clustering algorithm to classify inventory items at PT. Deli Farm Indonesia in order to improve inventory management efficiency and support managerial decision-making based on sales data patterns. This research employed a data mining approach using the Knowledge Discovery in Databases (KDD) framework. The research stages consisted of data collection, preprocessing, data transformation, clustering using the K-Means algorithm, and evaluation of clustering results. The implementation process was carried out using RapidMiner software with inventory transaction data obtained from PT. Deli Farm Indonesia. The implementation of the K-Means Clustering algorithm successfully classified inventory items into several clusters according to their sales characteristics and stock movement. The clustering results provide useful information for identifying fast-moving and slow-moving products, enabling the company to optimize inventory control, reduce excess stock, and improve inventory planning efficiency. The K-Means Clustering algorithm can be effectively applied to inventory management by providing meaningful classifications of inventory items. The resulting clusters can support strategic decision-making in warehouse management and contribute to more efficient inventory control at PT. Deli Farm Indonesia.

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References

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Published

2026-10-19

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How to Cite

Febry Yusmita. (2026). Implementation of K-Means Clustering for Inventory Item Classification in Inventory Management . Hanif Journal of Information Systems , 4(1), 95–100. Retrieved from https://journal.ilmubersama.com/index.php/hanif/article/view/131

Issue

Vol. 4 No. 1 (2026): August Edition

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Articles

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Copyright (c) 2026 Febry Yusmita

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