Data Mining Using K-Means Algorithm for Clustering Snack Sales at CV Sinar Pangan Utama

Bintang Tri Admaja

doi:10.59934/jaiea.v5i1.1520

Authors

Bintang Tri Admaja STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v5i1.1520

Keywords:

Clustering, K-Means, Sales Analysis, Snack Products, CV Sinar Pangan Utama

Abstract

Sales activities are a fundamental component of a company’s operations in achieving profitability. CV Sinar Pangan Utama, a company specializing in the production of snacks such as morena, pang pang, amazon, and kue bawang, faces challenges related to inventory surplus and limited insights into consumer behavior. This study aims to apply data mining techniques, specifically the K-Means clustering algorithm, to analyze sales data and identify product groupings based on sales performance. By classifying products into clusters of high and low demand, the company can derive actionable insights to optimize production planning, inventory management, and marketing strategies. The research utilizes sales data spanning from January to December 2023 and is implemented using a PHP and MySQL-based application. The findings are expected to contribute to more efficient decision-making processes by uncovering purchasing patterns, thereby enhancing the company’s responsiveness to market demand and improving overall business performances

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