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All Journal Jurnal Simetris Sistemasi: Jurnal Sistem Informasi Creative Information Technology Journal Faktor Exacta AL-ATHFAL : JURNAL PENDIDIKAN ANAK Jurnal Mantik Penusa JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Respati Madani : Indonesian Journal of Civil Society Jurnal Teknik Informatika (JUTIF) Jurnal Abdi Masyarakat Indonesia Aptekmas : Jurnal Pengabdian Kepada Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bangsa Inovasi Jurnal Pengabdian Masyarakat (IJPM) MUALLIMUN : JURNAL KAJIAN PENDIDIKAN DAN KEGURUAN Jurnal Pendidikan Sains dan Teknologi Terapan Jurnal Al-Husna
Norhikmah Norhikmah
Sistem Informasi, Fakultas Ilmu Komputer, Universitas AMIKOM Yogyakarta, Indonesia
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Journal : Jurnal Teknik Informatika (JUTIF)

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Regression Based Prediction of Roblox Game Popularity Using Extreme Gradient Boosting with Hyperparameter Optimization Amalina, Inna Nur; Norhikmah, Norhikmah; Ariyus, Dony; Koprawi, Muhammad; Prasetyo, Rafli Ilham
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5648

Abstract

The rapid growth of the digital gaming industry has increased the importance of predicting game popularity on user-generated content platforms such as Roblox, where diverse games and highly variable user engagement patterns create challenges in modeling long-term popularity trends. This study aims to develop a regression-based popularity prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on user interaction indicators, including visits, likes, dislikes, favorites, and active players. To investigate the effect of model optimization, hyperparameter tuning is performed using GridSearchCV. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Experimental results show that the baseline XGBoost model achieves an R² value of 80.74%, indicating strong capability in capturing non-linear popularity patterns. However, the optimized model yields a lower R² value of 77.71%, accompanied by slight increases in prediction error metrics, revealing that hyperparameter optimization does not always improve performance for highly skewed popularity data. Feature importance analysis further indicates that interaction-based attributes, particularly likes and dislikes, are the most influential predictors. These findings provide an important contribution to Informatics research by demonstrating the effectiveness of ensemble regression models for digital entertainment analytics while highlighting the need for critical evaluation of optimization strategies rather than assuming universal performance gains.
Co-Authors Abda Abda Acihmah Sidauruk Afifah, Neela Aldis Gandi Mitra Sanjung Ali Mustofa Alvien Muhammad Kannabi Amalina, Inna Nur Anggit Dwi Hartanto Anggun Pratiwi, Cut Tesya Iftillah Arifin, Muhammad Nurul Ashari, Wahid Miftahul Donni Prabowo Dony Ariyus Fajri, Ika Nur Hairunnisa Hairunnisa Helmawati, Nita Husain, Aghnaita imadduddin, Imadduddin Koprawi, Muhammad Kusrini Kusrini Kusuma, Prita Widia M. Rudyanto Arief M. Yogi Riyantama Isjoni Mastha Cahyaningrum, Dewi Pratama Muharam, Fatta Mu’alif Lihawa Mu’arif, Zidan Nita Helmawati Nor Latifah Nugroho, Adytyo Wahyu Nurma, Nurma Nurwijayanti Pirasintiya, Pirasintiya Pradana, Sulistyo Prasetyo, Rafli Ilham Pratama, Rifqhy Rayhan Andi Riga Pratama, Rifqy Rayhan Andi Riga Purwanto, Dimas Yudistira Randani, Randani Ratna Yulinda Rianda, Farhan Riski Rizka Mulya Miranti Rizky Febriyani Putri Rumini Rumini - Rumini Rumini Salsabila, Nur Syifa Setyaningsih, Adila Vidyana Arsanti Wiji Nurastuti Wiwi Widayani Yulianti, Fintas