Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization - Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al (LPPM Universitas Bina Sarana Informatika, 2024)
Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.