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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika dan Teknik Elektro Terapan JUTIK : Jurnal Teknologi Informasi dan Komputer JURIKOM (Jurnal Riset Komputer) Jurnal Kridatama Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing Aptekmas : Jurnal Pengabdian Kepada Masyarakat Journal of Computers and Digital Business
Dananjaya, Md Wira Putra
Universitas Pendidikan Nasional
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Journal : Journal of Computers and Digital Business

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Beyond Binary Classification: Time-to-Event Modeling for Player Retention Using Cox Proportional Hazards and Ensemble Learning Md. Wira Putra Dananjaya
Journal of Computers and Digital Business Vol. 5 No. 1 (2026)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i1.792

Abstract

Player retention is the primary economic driver in the Free-to-Play (F2P) gaming industry, yet traditional churn prediction methodologies often rely solely on binary classification, neglecting the critical temporal dimension of when a player is likely to leave. Furthermore, the scarcity of open-source behavioral datasets restricts the development of reproducible academic frameworks. This study addresses these gaps by proposing a hybrid analytical framework that integrates Ensemble Learning (XGBoost) for predictive precision and Survival Analysis (Cox Proportional Hazards) for time-to-event risk modeling, utilizing a realistically simulated dataset of 5,000 players. Experimental results indicate that while the XGBoost model achieves robust discriminative stability with an AUC of roughly 0.90, the Survival Analysis provides deeper explanatory insights, revealing that game progression (level_reached) is a significantly more dominant determinant of retention (Hazard Ratio < 1.0) than short-term recency metrics. These findings suggest that depth of commitment acts as a stronger buffer against churn than login frequency, offering game developers a quantifiable basis to shift retention strategies from generic daily incentives to progression-based milestones. By openly providing the simulated dataset and full analytical pipeline, this work also contributes a reproducible methodological template for future game analytics research in data-scarce environments.
Co-Authors Alicia Alfia Asokawati Amritha, Yadhurani Dewi Candrawengi, Ni Luh Putu Ika Darmaastawan, Kadek Dayanti, Made Ari Riska Gede Humaswara Prathama Gede Indrawan Gusti Agung Ayu Putri I Gusti Ngurah Darma Paramartha Ni Luh Putu Ika Candrawengi Pujayanti, Putu Gita Putu Gita Pujayanti Putu Gita Pujayanti Putu Gita Pujayanti Putu Wira Buana Sariyasa . Yadhurani Dewi Amritha