Analisis Klasterisasi Kualitas Internet Seluler Menggunakan Metode K-Means dan Gaussian Mixture Model - Irwansyah, Muhammad Aziiz; Meiriza, Allsela; Lestarini, Dinda (Forum Kerjasama Pendidikan Tinggi, 2025)
This study utilizes internet network data from Ookla Open Data (Speedtest Global Performance), comprising three main variables: download speed, upload speed, and latency. The aim is to analyze the condition and performance of mobile internet networks across 17 regencies/cities in South Sumatera Province in 2025 and to provide data-driven recommendations for the Department of Communication and Informatics to promote equitable and improved digital infrastructure through a Knowledge Discovery in Databases (KDD) approach. The applied methods include RobustScaler for data normalization, Principal Component Analysis (PCA) for dimensionality reduction, and K-Means and Gaussian Mixture Model (GMM) algorithms for clustering regions based on network characteristics. The analysis shows that both algorithms form three clusters (K=3) with distinct patterns. GMM demonstrates higher stability than K-Means, achieving a Silhouette score of 0.426 and Davies–Bouldin Index of 0.284, compared to K-Means with 0.351 and 0.688, while the lower Calinski–Harabasz score of GMM (9.960) indicates a trade-off between cluster compactness and stability, highlighting its adaptive behavior to data variation. Urban areas such as Palembang and Prabumulih belong to the high-performance cluster, whereas Ogan Komering Ulu Selatan lies in the low-performance cluster (18.87 Mbps; 33 ms), revealing a digital gap of approximately 18 Mbps across regions. These findings emphasize the need for equitable digital infrastructure strategies through fiber-optic expansion, BTS capacity enhancement, and multi-stakeholder collaboration toward Indonesia’s Digital Vision 2045.