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

[1] A. Ariyanto, R. Bangun, M. R. M. Indillah, A. F. M. Trenggana, D. R. Sholihah, M. Ariyanti, et al., Manajemen Pemasaran. Medan, Indonesia: Yayasan Kita Menulis, 2023.

[2] A. Selay, G. D. Andigha, A. Alfarizi, M. I. B. Wahyudi, M. N. Falah, M. Encep, and M. Khaira, "Sistem Informasi Penjualan," Karimah Tauhid, vol. 2, no. 1, pp. 232–237, 2023.

[3] S. Aulia, "Klasterisasi Pola Penjualan Pestisida Menggunakan Metode K-Means Clustering (Studi Kasus di Toko Juanda Tani Kecamatan Hutabayu Raja)," Djtechno: Jurnal Teknologi Informasi, vol. 1, no. 1, pp. 1–5, 2020.

[4] F. Handayani, "Aplikasi Data Mining Menggunakan Algoritma K-Means Clustering untuk Mengelompokkan Mahasiswa Berdasarkan Gaya Belajar," Jurnal Teknologi dan Informasi, vol. 12, no. 1, pp. 46–63, 2022.

[5] P. Apriyani, A. R. Dikananda, and I. Ali, "Knowledge Discovery in Database pada Data Mining," 2023.

[6] Z. Zulham et al., "Implementasi Knowledge Discovery in Database Menggunakan RapidMiner," 2023.

[7] N. Rahmawita et al., "Database sebagai Sistem Penyimpanan Data Terintegrasi," 2023.

[8] A. Gunawan, "Hubungan Persediaan Dengan Penjualan Pada Laporan Keuangan Koperasi Bukit Muria Jaya Tahun 2021–2022," Jurnal PBM: Pengembangan Bisnis dan Manajemen, vol. 23, pp. 95–109, 2023.

[9] N. Sari, "Perencanaan dan Pengendalian Persediaan Barang dalam Upaya Meningkatkan Efektivitas Gudang," Jurnal Bisnis, Logistik dan Supply Chain (BLOGCHAIN), vol. 2, no. 2, pp. 85–91, 2022.

[10] A. M. D. Putri, "Analisis Pengendalian Stok Beras Komersial dengan Metode Economic Order Quantity (EOQ) dan Reorder Point (ROP) di Perum Bulog Cabang Surakarta," 2025.

[11] I. Darmansyah, "Sistem Informasi Persediaan Barang pada Rumah Sakit Umum Daerah Kabupaten Batu Bara," SABER: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi, vol. 3, no. 1, pp. 169–177, 2025.

[12] R. Rahman, "Data Mining sebagai Pendukung Pengambilan Keputusan," 2020.

[13] Haryanti et al., "Penerapan Data Mining untuk Meningkatkan Efisiensi Operasional Perusahaan," 2024.

[14] Setiyani et al., "Fungsi Data Mining dalam Analisis Data," 2020.

[15] B. Buulolo, Data Mining untuk Perguruan Tinggi. Medan, Indonesia, 2020.

[16] O. Ovilianda, "Knowledge Discovery in Database sebagai Pendukung Sistem Pengambilan Keputusan," 2021.

[17] Purba et al., "Pengendalian Internal Persediaan Barang," 2021.

[18] Setiawan, "Manajemen Persediaan Barang," 2021.

[19] Rukmana et al., Database Management System. Bandung, Indonesia, 2023.

[20] Srisulistiowati et al., "Pemanfaatan RapidMiner dalam Implementasi Data Mining," 2021.

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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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