Komparasi Metode K-Means dan Hierarchical Clustering untuk Pengelompokan Pola Bermain Pemain Mobile Legends

Savior Podung; Reynaldo Joshua Salaki

doi:10.30606/rjti.v5i2.4692

Authors

Savior Podung Universitas Sam Ratulangi
Reynaldo Joshua Salaki Universitas Sam Ratulangi

DOI:

https://doi.org/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.

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