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Fajar AR, Muh. Aqil
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Computer Science & IT

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K-Means and K-Medoid in Clustering Analysis of Network Congestion Level Darwis, Herdianti; Purnawansyah, Purnawansyah; Umalekhoa, Alfi Syahrin; Adnan, Adam; Salim, Yulita; Umar, Fitriyani; Raja, Roesman Ridwan; Fajar AR, Muh. Aqil
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2083.323-335

Abstract

This research investigates the application of clustering techniques to network congestion data at Universitas Muslim Indonesia, employing a hybrid metric approach based on packet loss and delay. The study utilized two algorithms, K-Means and K-Medoid, applied in a semi-supervised scenario to group 255,147 network data points into 3, 4, and 5 clusters, considering 10 principal variables. During the pre-processing phase, data cleansing was conducted to address missing values, followed by normalization to standardize the scale of numerical variables, thereby preparing the data for the clustering process. Model validation was performed using four cluster evaluation methods: Gap Statistic, Davies-Bouldin Index, and Elbow Method. The evaluation results indicate that both algorithms were capable of forming valid and reliable clusters. However, the K-Means algorithm demonstrated superior performance compared to K-Medoid, particularly when utilizing three Quality of Service variables: throughput, packet loss, and delay. In this configuration, K-Means yielded more stable clusters, a clearer separation between clusters, and a more structured visualization. Consequently, K-Means is considered more optimal for classifying network congestion levels and presents an effective approach for network data segmentation
Co-Authors Adnan, Adam Darwis, Herdianti Purnawansyah Purnawansyah Raja, Roesman Ridwan Salim, Yulita Umalekhoa, Alfi Syahrin Umar, Fitriyani