Material Sales Clustering Using the K-Means Method

Authors

  • Sri Rahayuni STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Indra Gunawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Ika Okta Kirana STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i1.177

Keywords:

Data Mining, Sales, K-Means, Family Gypsum

Abstract

Along with the increasing growth of technology and the development of science, business competition is also getting faster and therefore we are required to always develop the business in order to always survive in the competition. Family Gypsum is a store whose sales system is the same as a supermarket, namely the buyer will take the goods to be purchased himself. From this, data on sales, purchases of goods, and unexpected expenses are not structured properly so that the data only functions as an archive. In this research, data mining is applied using the K-Means calculation process which provides a standard process for using data mining in various fields to be used in clustering because the results of this method are easy to understand and interpret. The results obtained from the K-Means method that has been implemented into Rapid Miner have the same value, which produces 3 clusters, namely clusters that do not sell, clusters that sell, and clusters that sell very well. With red clusters with 2 items, the clusters selling green with 28 items, the clusters selling with blue with 30 items. The results of this study can be entered into the Family Gypsum store Jl. H. Ulakma Sinaga, Red Rambung who is getting more attention on each sale based on the cluster that has been done

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Published

2022-03-18

How to Cite

Rahayuni, S., Gunawan, I., & Kirana, I. O. (2022). Material Sales Clustering Using the K-Means Method. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(1), 85–94. https://doi.org/10.55123/jomlai.v1i1.177

Issue

Vol. 1 No. 1 (2022): March

Section

Articles

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Copyright (c) 2022 Sri Rahayuni, Indra Gunawan, Ika Okta Kirana

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