EXPLORING ONLINE COMMUNITY PERCEPTIONS OF MACHINE LEARNING USE IN ADJUSTING GAME DIFFICULTY LEVELS - Mahesha Jenar Yushayahya; Muhammad Rizky Dirwansyah; Ilham Saputra; Arya Farija Putra; Muhammad Rezy Maulidiansyah (Algebra : Jurnal Pendidikan, Sosial dan Sains, 2026)
This study aims to explore the online community's perception of the use of machine learning (ML) in dynamic difficulty adjustment (DDA) through computational text analysis. The use of ML in DDA has become an important area in game design due to its ability to increase engagement and personalize the gaming experience, but it has sparked debate regarding the fairness and transparency of the system. This study uses a descriptive quantitative approach by analyzing 3,016 comments from the Reddit and Steam communities using sentiment analysis, topic modeling, and word frequency analysis techniques. The sentiment analysis results show a dominance of positive sentiment (63.8%), followed by negative sentiment (20.9%) and neutral sentiment (15.0%). Topic analysis identified five main themes: Gameplay & Difficulty Adjustment, General Enjoyment & Perception, Narrative Experience & Balance, Combat Mechanics & Fairness, and Technical Reflection & Suggestion. Word frequency analysis showed the community's focus on the balance between challenge (difficulty) and enjoyment (fun) in the gaming experience. The research findings indicate that the online community positively accepts the application of ML in DDA, with the important notes of system transparency, fairness of mechanisms, and balance between technical challenges and players' emotional satisfaction. This research contributes to interdisciplinary studies between game studies, artificial intelligence, and computational social science, and serves as a reference for game developers in designing adaptive systems that are technically effective and accepted by players.