K-Means Clustering Implementation For Grouping Heavy Equipment Spareparts Data

Authors

  • Andira Rahmawati STMIK PPKIA Tarakanita Rahmawati
  • Muhammad Fadlan STMIK PPKIA Tarakanita Rahmawati
  • Anto Anto STMIK PPKIA Tarakanita Rahmawati

DOI:

https://doi.org/10.71302/jbidai.v7i2.37

Keywords:

data mining, grouping, k-means clustering, sparepart, centroid

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.

References

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Published

12/31/2024

How to Cite

Rahmawati, A., Fadlan, M., & Anto, A. (2024). K-Means Clustering Implementation For Grouping Heavy Equipment Spareparts Data. Journal of Big Data Analytic and Artificial Intelligence, 7(2), 33–38. https://doi.org/10.71302/jbidai.v7i2.37

Issue

Vol. 7 No. 2 (2024): JBIDAI Desember 2024

Section

Articles

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