Klasterisasi Pola Penggunaan Internet Mahasiswa Menggunakan Pendekatan Principal  Component Analysis dan K-Means Clustering

Maxtulus Junedy Nababan; Anil Hakim Syofra; Desniarti Desniarti

doi:10.62007/joupi.v4i2.720

Authors

Maxtulus Junedy Nababan Universitas Mataram
Anil Hakim Syofra Universitas Asahan
Desniarti Desniarti Universitas Muslim Nusantara Al-Washliyah

DOI:

https://doi.org/10.62007/joupi.v4i2.720

Keywords:

Internet Usage, K-Means Clustering, Machine Learning, Principal Component Analysis, User Segmentation

Abstract

This study aims to map and identify patterns of internet usage behavior based on usage functions and time management. As internet utilization continues to increase among students, a deeper understanding of user behavior characteristics is essential to formulate effective policies that encourage productive internet use. This research employed a quantitative approach supported by statistical analysis and machine learning techniques. Principal Component Analysis (PCA) was applied to reduce data dimensionality, while K-Means Clustering was utilized to objectively classify internet users into groups with similar behavioral characteristics. The PCA results successfully reduced 12 manifest indicators into two principal components: the dimension of structured academic utility and entertainment, and the dimension of usage intensity and dependence on internet facilities. These two latent components explained 61.5% of the total variance in internet usage behavior indicators. Furthermore, the K-Means Clustering algorithm identified three optimal clusters: academic-functional internet users (n = 85), consumptive and dependent internet users (n = 88), and independent and economical internet users (n = 87). The findings indicate that internet users exhibit diverse behavioral patterns, requiring cluster-based intervention strategies tailored to the characteristics of each group. Therefore, implementing cluster-oriented policies within educational institutions is crucial for improving the effectiveness, efficiency, and productivity of internet utilization while promoting more targeted and responsible digital behavior.

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