Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal ROUTERS: Jurnal Sistem dan Teknologi Informasi
Naufaldi Favian Archi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Customer Segmentation Based on Spending Patterns Using K-Means Clustering and PCA Dzakwan Akbar Perdana Wijaya; sasmita, Chesie fenta; Naufaldi Favian Archi
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 2, Juli 2025 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i2.3923

Abstract

Companies face challenges in understanding customer spending patterns, which can lead to ineffective marketing strategies. Traditional customer segmentation approaches often fail to accurately identify groups with different consumption behaviors. Therefore, this study proposes the implementation of the K-Means algorithm combined with Principal Component Analysis (PCA) to segment customers based on their spending patterns. This study uses a dataset containing customer spending information across various product categories, including wine, meat, fish, sweets, fruits, and gold. The Elbow method is applied to determine the optimal number of clusters, followed by K-Means clustering. The results are visualized using PCA to facilitate the interpretation of customer spending patterns. The findings indicate that the optimal number of clusters is six, with the Within-Cluster Sum of Squares (WCSS) decreasing from 50,000 for one cluster to 29,000 for six clusters. Cluster 3 exhibits the highest spending, particularly on meat at 566.91 and fish at 183.58, whereas Cluster 0 has the lowest spending, with its highest value being only 91.60 for wine. Silhouette Score evaluation shows that K-Means achieves a score of 0.4745, outperforming the Gaussian Mixture Model (GMM) with 0.0674
Co-Authors Dzakwan Akbar Perdana Wijaya sasmita, Chesie fenta