Implementasi Algoritma K-Means Pada Sistem Persediaan Barang
DOI:
https://doi.org/10.47065/bulletincsr.v6i2.1017Keywords:
Data Mining; Inventory; K-Means Clustering; ClusteringAbstract
Inventory is an important component in a company's operational activities, especially in the trade sector, because it directly affects the smoothness of sales and the level of customer satisfaction. Unstructured inventory management can lead to stockpiling of goods, stock shortages, and inappropriate decision making. Maisa Building Materials Store located in Semerap, Kerinci Regency, Jambi, currently still records inventory manually, so the shop owner has difficulty in identifying items with high and low sales levels. This study aims to implement the K-Means Clustering algorithm in grouping inventory based on sales levels to support more effective and efficient stock management. The research method used is data mining with the stages of data collection, preprocessing, manual calculation of the K-Means algorithm, and implementation using RapidMiner software. The analyzed data amounted to 507 inventory items that have gone through a data cleaning process so that they are suitable for use in grouping. Grouping is done with two clusters, namely a cluster of goods with a low sales level and a cluster of goods with a high sales level. The results of the study indicate that 494 items, or 97.44 percent, fall into the low-sales cluster, while 13 items, or 2.56 percent, fall into the high-sales cluster. These results indicate that most products have relatively low sales turnover, while only a small proportion contribute significantly to total store sales. The information generated from this clustering process can be used as a basis for decision-making in inventory management, particularly in determining stocking priorities, stock control, and developing appropriate, data-driven marketing strategies.
Downloads
References
R. Y. Simanullang, P. Wanny, and S. M. Rambe, “Implementation of the K-Means Algorithm on Smart Systems in Grouping Gadget Accessory Purchase Patterns,” JITCSE (Journal Inf. Technol. Komput. Sci. Electr. Eng., vol. 2, no. 3, pp. 22–31, 2026, doi: 0.61306/jitcse.
D. Riswanda and A. T. Priandika, “Analisis dan Perancangan Sistem Informasi Manajemen,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 2, no. 1, pp. 94–101, 2021.
A. Adiyanto and Y. Arie Wijaya, “Penerapan Algoritma K-Means Pada Pengelompokan Data Set Bahan Pangan Indonesia Tahun 2022-2023,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1344–1350, 2023, doi: 10.36040/jati.v7i2.6849.
F. Febriansyah and S. Muntari, “Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 8, no. 1, pp. 66–77, 2023, doi: 10.14421/jiska.2023.8.1.66-77.
A. Patappari and N. Muhlisa, “Sistem Informasi Persediaan Barang Berbasis Web Pada Toko Throve Store Soppeng,” J. Ilm. Sist. Inf. dan Tek. Inform., vol. 6, no. 1, pp. 1–8, 2023, doi: 10.57093/jisti.v6i1.142.
A. Putera, U. Siahaan, N. A. Harahap, and R. Y. Simanullang, “Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset,” J. BIT (Bulletin Inf. Technol., vol. 6, no. 1, pp. 33–39, 2025, doi: 10.47065/bit.v5i2.1830.
P. S. Jetlie Kang, “Analisis Pola Penjualan Produk Elektronik Pada E-Commerce Menggunakan Algoritma FP-Growth,” J. Comasie, vol. 01, 2025.
R. Wahyusari, “Penerapan Algortima FP-Growth Untuk Menemukan Pola Peminjaman Alat Pada Workshop Teknik Mesin,” Log. J. Ilmu Komput. dan Pendidik., vol. 1, no. 3, pp. 406–411, 2023, [Online]. Available: https://journal.mediapublikasi.id/index.php/logic/article/view/2745%0Ahttps://journal.mediapublikasi.id/index.php/logic/article/download/2745/1211
B. G. Sudarsono, M. I. Leo, A. Santoso, and F. Hendrawan, “Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner,” JBASE - J. Bus. Audit Inf. Syst., vol. 4, no. 1, pp. 13–21, 2021, doi: 10.30813/jbase.v4i1.2729.
A. Supriyadi, A. Triayudi, and I. D. Sholihati, “Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 2, pp. 229–240, 2021, doi: 10.29100/jipi.v6i2.2008.
I. Maryani, O. Revianti, H. M. Nur, and S. Sunanto, “Implementasi Data Mining Pada Penjualan Di Toko GOC Kosmetik Dengan Menggunakan Metode Algoritma Apriori,” Indones. J. Softw. Eng., vol. 8, no. 1, pp. 92–98, 2022.
P. M. S. Tarigan, J. T. Hardinata, H. Qurniawan, M. Safii, and R. Winanjaya, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang: Studi Kasus: Toko Sinar Harahap,” J. Janitra Inform. dan Sist. Inf., vol. 2, no. 1, pp. 9–19, 2022.
E. Marlena et al., “Implementasi Data Mining Untuk Memprediksi Status Gizi Anak Balita Pada Puskesmas Gedung Sari Menggunakan Polynomial Regression,” J. Teknol. Komput. dan Sist. Inf., vol. 6, no. 2, pp. 193–196, 2023.
H. Mustakim and S. Priyanta, “Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 16, no. 2, p. 113, 2022, doi: 10.22146/ijccs.68903.
L. Al-Alawi, J. Al Shaqsi, A. Tarhini, and A. S. Al-Busaidi, “Using machine learning to predict factors affecting academic performance: the case of college students on academic probation,” Educ. Inf. Technol., vol. 28, no. 10, pp. 12407–12432, 2023, doi: 10.1007/s10639-023-11700-0.
J. Maulani and M. Sari, “Komparasi Metode K-Nearest Neighbor (Knn) Dengan Support Vector Machine (Svm) Terhadap Tingkat Akurasi Klasifikasi Kualitas Air,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 12, no. 2, pp. 430–435, 2023, doi: 10.30591/smartcomp.v12i2.4205.
P. A. Myers et al., “pyMAISE: A Python platform for automatic machine learning and accelerated development for nuclear power applications,” Prog. Nucl. Energy, vol. 180, p. 105568, 2025.
F. Ekundayo, I. Atoyebi, A. Soyele, and E. Ogunwobi, “Predictive analytics for cyber threat intelligence in fintech using big data and machine learning,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–15, 2024.
D. P. Indini, Mesran, and Dito Putro Utomo, “Penerapan Data Mining Dalam Pengelompokan Data Reseller di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means,” J. Ilm. Media Sisfo, vol. 17, no. 2, pp. 189–202, 2023, doi: 10.33998/mediasisfo.2023.17.2.1391.
R. Dila, S. Defit, and S. Arlis, “Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas ( SMA ),” vol. 5, no. 5, pp. 1113–1119, 2025, doi: 10.47065/bulletincsr.v5i5.751.
J. Yandi and K. Wijaya, “Rancang Bangun Aplikasi Persediaan Barang Pada Counter Karya Cellmenggunakan Vb Net,” JSK (Jurnal Sist. Inf. dan Komputerisasi Akuntansi), vol. 7, no. 1, pp. 1–6, 2023, doi: 10.56291/jsk.v7i1.109.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Algoritma K-Means Pada Sistem Persediaan Barang
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Achsyanul Khaliq, Ismasari Nawangsih, Annisa Maulana Majid
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
