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All Journal Journal of Big Data Analytic and Artificial Intelligence
Andira Rahmawati
STMIK PPKIA Tarakanita Rahmawati
Computer Science & IT

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Implementasi K-Means Clustering Untuk Mengelompokan Data Sparepart Alat Berat Andira Rahmawati; Muhammad Fadlan; Anto Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.37

Abstract

Data mining is a crucial process for extracting valuable information from existing data, which can then be used by companies for quick and accurate decision-making. One of the commonly used methods in data mining is the K-Means Clustering method. In this study, the author applied K-Means Clustering in the retail sector to address the challenges faced by PT. Patria Jaya Mandiri. The author designed an application that can cluster heavy equipment spare parts based on sales data, with the aim of helping the company identify which spare parts are most favored by consumers. This clustering is expected to simplify the process of determining optimal spare part stock, ultimately positively impacting the company’s revenue. The results of this study indicate that heavy equipment spare parts can be categorized into three groups: Most Popular, Popular, and Least Popular. Cluster 1 (Most Popular) consists of 3 data points, Cluster 2 (Popular) consists of 39 data points, and Cluster 3 (Least Popular) consists of 8 data points. This clustering result can serve as a guide for PT. Patria Jaya Mandiri in determining the optimal spare part inventory in the future.
Co-Authors Anto Anto MUHAMMAD FADLAN