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All Journal Riau Jurnal Teknik Informatika
Savior Podung
Universitas Sam Ratulangi
Computer Science & IT

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Komparasi Metode K-Means dan Hierarchical Clustering untuk Pengelompokan Pola Bermain Pemain Mobile Legends Savior Podung; Reynaldo Joshua Salaki
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4692

Abstract

The rapid growth of Mobile Legends: Bang Bang esports produces large volumes of gameplay statistics that remain underutilized beyond individual match summaries. This study compares K-Means and Hierarchical Clustering to group player playstyles using five gameplay variables (kill, death, assist, gold, and match duration) from 990 valid records in the MPL Cambodia Season 6 - BoxMatch dataset, following CRISP-DM preprocessing and Min-Max normalization. The Elbow Method identified K=3 as the optimal number of clusters. K-Means produced three interpretable groups - Carry/Damage Dealer, Late Game Fighter, and Sacrificial/Tank - with a Silhouette Score of 0.252 and a balanced distribution (42.6%, 18.5%, 38.9%), outperforming Hierarchical Clustering with Ward linkage (Silhouette Score 0.2203, less balanced distribution). The results were deployed into an interactive Flask-based web application for dataset upload, clustering visualization, and method comparison, which was validated through black box testing. This research demonstrates that combining clustering with web-based visualization can effectively reveal player playstyle patterns from competitive Mobile Legends gameplay statistics.
Co-Authors Salaki, Reynaldo Joshua