Classification of Spending Segmentation in Mobile Game Applications Using Random Forest and Decision Tree Algorithms

Putra Wicaksana, Dewa Restu
Anom, Rangga
Musyarafah, Syahrina
Giatika Chrisnawati

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 5 No. 2 (2026): February 2026

This research aims to classify spending segmentation in mobile game users using Random Forest and Decision Tree algorithms. The dataset consists of demographic attributes, gameplay behavior, session frequency, and historical spending records. Several preprocessing steps uwere applied, including missing value handling, label encoding, one-hot encoding, and feature scaling. The data were divided into an 80:20 training-testing ratio, and hyperparameter tuning was performed using GridSearchCV. The results indicate that Random Forest achieved higher accuracy compared to Decision Tree, demonstrating better generalization for multiclass segmentation (Low, Medium, High spenders). This study shows the potential of machine learning in predicting user spending behavior to support data-driven monetization strategies in mobile game applications.