Implementation of the K-Means Algorithm for Clustering Hot Selling and Less Selling Goods at XYZ Wholesalers

Silva Chaerani; Riki Andri Yusda; Rohminatin Rohminatin

doi:10.30865/jurikom.v12i4.8882

Authors

Silva Chaerani Universitas Royal Asahan Sumatera Utara
Riki Andri Yusda Universitas Royal Asahan Sumatera Utara
Rohminatin Rohminatin Universitas Royal Asahan Sumatera Utara

DOI:

https://doi.org/10.30865/jurikom.v12i4.8882

Keywords:

K-Means, Clustering, Product, Wholesalers, Data Mining

Abstract

Ratna Wholesale is a store that sells various household supplies, clothing, and other daily necessities. In managing the stock of goods, the owner still has difficulty in distinguishing products that are in demand and less in demand objectively, so that there is often an imbalance in the inventory of goods. This has an impact on the vacancy of products that are needed and the accumulation of products that are less in demand. To overcome these problems, this research aims to build a system that can group products based on sales levels using the K-Means clustering method. This research uses a quantitative approach with data collection techniques through observation, interviews, and sales documentation for the last three years. The system was developed using Visual Basic programming language and MySQL database. The results of the system implementation show that the K-Means method is effective in grouping products into two categories, namely hot selling items and less selling items, thus helping owners in making more efficient stock management decisions and increasing customer satisfaction. From the calculation of the system, it is obtained that the products that are in demand are 24% and the products that are less in demand are 76%.

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