E-Commerce Consumer Data Clustering Using K-Means Algorithm And Kaggle Dataset - Sabrina, Ade Eka; Yasin, Muhammad (Journal of Computer Science and Informatics Engineering, 2025)
Online shopping has become a part of people's lifestyle. This is because of the many conveniences obtained to meet primary to tertiary needs. The current condition, consumer purchase transaction history data has not been utilized optimally so that it is less effective. This makes the company feel that it has not been optimal in meeting customer expectations in increasing consumer loyalty. In addition, the current marketing strategy is also considered ineffective because the offers made by the company to each consumer are still general, the company has not offered products or promotions that are really needed by consumers. Through this study, the author tries to provide solutions to companies to increase the efficiency of marketing strategies that greatly influence increasing consumer loyalty by conducting clustering analysis of e-commerce consumer data. The purpose of this study is to design and create an application for clustering e-commerce consumer data using the k-means algorithm and the kaggle dataset. The data used in this study is e-commerce consumer data. The output results of solving the problem of clustering e-commerce consumer data. It can be seen based on the results of the payment data grouping for Cluster 1 (Electronic Payment): Total Payment: 72,000,000.00, Cluster 2 (Shoe Payment): Total Payment: 6,600,000.00, Cluster 3 (Clothing Payment): Total Payment: 2,940,000.00 and Cluster 4 (Cosmetic Payment): Total Payment: 105,000,000.00