Application of the K-Means Algorithm in the Segmentation of 3kg Lpg Customers

Ginaselvia Ananda; Nana  Suarna; Agus Bahtiar; Arif Rinaldi Dikananda; Faturrohman

doi:10.59934/jaiea.v5i2.1853

Authors

Ginaselvia Ananda STMIK IKMI Cirebon
Nana Suarna STMIK IKMI Cirebon
Agus Bahtiar STMIK IKMI Cirebon
Arif Rinaldi Dikananda STMIK IKMI Cirebon
Faturrohman STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v5i2.1853

Keywords:

K-Menas Clustering, Customer Segmentation, Sales Data Analysis, Marketing Strategi, Davies Bouldin Index

Abstract

This research was motivated by PT Sumber Perkasa Mandiri's need to understand the purchasing patterns of 3 kg LPG gas customers more accurately in order to improve the effectiveness of its marketing strategy. The purpose of this study was to apply the K-Means Clustering algorithm to form customer segmentation based on transaction behavior. The method used is a quantitative approach with sales data analysis of 850 records through the stages of data selection, preprocessing, attribute transformation, and modeling using RapidMiner Studio. Model evaluation was carried out using the Davies-Bouldin Index to determine the optimal number of clusters. The results of the study show the formation of two main clusters, namely the premium customer cluster with high purchase frequency and high loyalty, and the low-activity customer cluster that only makes purchases when necessary. The best DBI value at K=2 of 0.057 indicates excellent cluster separation quality. These findings conclude that K-Means Clustering is effective in identifying differences in consumption behavior, and its implications provide a strategic basis for companies to design loyalty programs for high-value customers and more intensive promotions for low-activity customers.

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