Early Warning System for the Impact of E-sports on Academics Based on Hybrid Naïve Bayes and Particle Swarm - Mohammad Syamsul Azis, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The integration of e-sports into the lifestyle of high school students in the era of industrial technology 4.0 brings a double dilemma between the development of digital skills and the risk of declining academic performance due to addiction. Previous research has successfully classified the impact of e-sports using the Naïve Bayes algorithm, but the model is static and only provides post-mortem analysis. This study proposes an Early Warning System (EWS) based on a hybrid of Naïve Bayes and Particle Swarm Optimization (PSO) designed to work dynamically and comprehensively. PSO is implemented to heuristically optimize attribute weights to overcome the weakness of the feature independence assumption in the pure Naïve Bayes algorithm. The test was carried out using the 10-fold cross-validation method on 178 student data of Madrasah Aliyah Negeri Rengasdengklok. The algorithm implementation resulted in an accuracy rate of 75.95% and an Area Under Curve (AUC) of 0.792. The main contribution of this research is the transformation from a traditional classification model to a proactive early warning system, where the system real-time monitors playing and studying duration, and then uses a probability threshold (Ƭ≥0.6) to trigger mitigation notifications to teachers and parents. In-depth analysis results show that behavioral variables such as playing duration have a much more massive level of significance (weight 1.0) compared to cognitive intelligence level or IQ (weight 0.001) in predicting academic failure. These findings provide a new paradigm for educational institutions in designing intervention strategies focused on time management and student digital literacy.