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Adha, Ismail Adhiya
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Computer Science & IT Electrical & Electronics Engineering

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Implementation of the K-Means Algorithm in Sales Clustering at a Company using the KDD Methodology Rochmawati, Milla; Bagaskara, Ganes Wisnu Cahya; Adha, Ismail Adhiya; Umaidah, Yuyun; Voutama, Apriade
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3074

Abstract

This research aims to implement K-Means algorithm in sales clustering at PT Sila Tirta Gemilang using Knowledge Discovery in Databases (KDD) methodology. PT Sila Tirta Gemilang is a company operating in the bottled drinking water industry sector. This research was conducted using a KDD approach that involves collecting historical sales data and has the main objective of improving the company's understanding of their product sales patterns. K-Means Clustering algorithm is used to classify products based on similar sales characteristics. In the K-Means method, the optimal cluster center point is determined to group products with comparable sales performance. By applying clustering using K-Means algorithm and KDD method, clustering of water types that are in significant demand at PT Sila Tirta Gemilang was conducted. As a result, three clusters were found, each containing water types with different characteristics. Cluster 0 has 1 water type with a high level of interest, while Cluster 1 has 3 water types with a low level of interest. Finally, Cluster 2 consists of 2 water types with a medium level of interest. From the results that have been obtained, companies can take more appropriate steps to increase profits and optimize their sales performance.
Co-Authors Apriade Voutama Bagaskara, Ganes Wisnu Cahya Rochmawati, Milla Umaidah, Yuyun