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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Ade Linhar P.
Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan
Computer Science & IT Economics, Econometrics & Finance

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Segmentasi Pasar Terhadap Generasi Z Berdasarkan Perilaku Belanja Online Menggunakan Metode K-Means Clustering Meri Nova Marito; Ratna Wati Simbolon; Duma Lasmaria Siagian; Bertha Nerpy Siahaan; Ade Linhar P.; Anugrah Zai
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp244-248

Abstract

The advancement of digital technology greatly affects consumption patterns, especially among Gen Z, towards e-commerce platforms. Gen Z has a high affinity for digital technology. This condition creates a need for more precise market segmentation so that marketing strategies can be tailored to consumer characteristics. This study aims to conduct market segmentation based on online shopping behavior using the K-Means Clustering method. The data used in this study is the Online Retail Dataset obtained from the UCI Machine Learning Repository. The research stages include data preprocessing, attribute selection, data normalization, determination of the number of clusters, and the clustering process using the K-Means algorithm. The variables analyzed include transaction frequency, product purchase quantity, customer transaction value, and transaction time interval. The research results show that customer data can be grouped into several segments with different characteristics, such as active customers with high purchase levels, customers with moderate transactions, and customers with low purchasing activity. Thus, the resulting segmentation can help business actors understand consumer behavior and develop more targeted marketing strategies. In addition, the K-Means Clustering method has been proven effective in grouping customer data based on online shopping patterns. 
Co-Authors Anugrah Zai Duma Lasmaria Siagian Meri Nova Marito SIAHAAN, BERTHA NERPY Simbolon, Ratna Wati