K-Means and K-Medoid in Clustering Analysis of Network Congestion Level


Herdianti Darwis(1*); Purnawansyah Purnawansyah(2); Alfi Syahrin Umalekhoa(3); Adam Adnan(4); Yulita Salim(5); Fitriyani Umar(6); Roesman Ridwan Raja(7); Muh. Aqil Fajar AR(8);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(4) Universitas Muslim Indonesia
(5) Universiti Kuala Lumpur
(6) Universitas Muslim Indonesia
(7) Kyushu Institute of Technology
(8) Universitas Muslim Indonesia
(*) Corresponding Author

  

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

Keywords


Network Congestion Clusters; Davies Bouldin Index; Elbow method; Gap-Statistic; K-Means; QoS Parameters

  
  

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doi  https://doi.org/10.33096/ilkom.v17i3.2083.323-335 		  

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Copyright (c) 2025 Herdianti Darwis, Purnawansyah Purnawansyah, Alfi Syahrin Umalekhoa, Adam Adnan, Yulita Salim, Fitriyani Umar, Roesman Ridwan Raja, Muh. Aqil Fajar AR

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