Penerapan Algoritma K-Means Clustering untuk Mengelompokkan Pola Bermain Pemain TheoTown
DOI:
https://doi.org/10.46880/jmika.Vol10No2.pp513-523Keywords:
K-Means Clustering, TheoTown, Player Behavior, Data Mining, Mixed-MethodAbstract
City-building simulation games like TheoTown trigger diverse player behaviors, yet the segmentation between strategic and satirical players remains unmapped. This study classifies TheoTown player patterns using the Knowledge Discovery in Databases (KDD) framework and k-means clustering on 1,084 respondents. Data were preprocessed with one-hot encoding and min-max scaling to eliminate Euclidean distance bias. Multi-criteria validation, specifically the Silhouette Coefficient, indicated the most rational separation at K=2 (score 0.1758). The novelty of this study lies in the mixed-method approach integrating K-Means with multi-criteria validation and qualitative content analysis in a sandbox ecosystem. Quantitative results were validated qualitatively via word clouds from respondents' essays. Findings reveal two main archetypes: Cluster 0 (Strategic & Welfare Builders, 57.29%), focusing on economic and spatial efficiency, and Cluster 1 (Experimental & Satirical Players, 42.71%), utilizing the game for radical experiments and government critique. This study indicates that simulation games can serve as a medium for players' psychographic expression.
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