Segmentasi Pelanggan Berdasarkan Kebutuhan Primer Skunder dan Tersier Menggunakan K-Means Clustering

Caesaliana Indah Mu’assyaroh Indah; M. Rizieq Sultan Arkan; Galuh Sitoresmi Galuh; Sabrina Rizkiya Sabrina; Zida Nadhifah Aulia Kencana Zida

Segmentasi Pelanggan Berdasarkan Kebutuhan Primer Skunder dan Tersier Menggunakan K-Means Clustering

Authors

Caesaliana Indah Mu’assyaroh Indah Institut Informatika dan Bisnis Darmajaya
M. Rizieq Sultan Arkan Student
Galuh Sitoresmi Galuh Institut Informatika dan Bisnis Darmajaya
Sabrina Rizkiya Sabrina Institut Informatika dan Bisnis Darmajaya
Zida Nadhifah Aulia Kencana Zida Institut Informatika dan Bisnis Darmajaya

Abstract

In the digital marketing era, companies are required to deeply understand customer behavior in order to develop targeted strategies. Customer segmentation is a common technique used to group customers based on similarities in their characteristics and consumption behaviors. This study aims to identify customer segments using unsupervised learning techniques with the K-Means clustering algorithm. The dataset, obtained from Kaggle, contains 2,240 customer records with demographic and purchase behavior attributes. The six primary features analyzed include Income, Age, TotalChildren, MntMeatProducts, NumCatalogPurchases, and Recency. The clustering results reveal distinct customer groups with different characteristics and purchasing tendencies, which can be used to develop more personalized and efficient marketing strategies.

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Published

2025-11-30

How to Cite

Indah, C. I. M., Arkan, M. R. S., Galuh, G. S., Sabrina, S. R., & Zida, Z. N. A. K. (2025). Segmentasi Pelanggan Berdasarkan Kebutuhan Primer Skunder dan Tersier Menggunakan K-Means Clustering. Journal of Data Science Methods and Applications, 1(2), 102–112. Retrieved from https://journal.darmajaya.ac.id/index.php/JoDMApps/article/view/1129