Pola Perilaku Pemain Roblox: Pemodelan Klasifikasi Berbasis Naïve Bayes - Risqi Nur Avianti; Cucut Hariz Pratomo (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
The development of digital technology has driven the growth of online gaming as a medium for entertainment, social interaction, and creativity development. One platform that has grown rapidly is Roblox, which allows users to play, interact, and create digital content. This diversity of activities causes player behavior characteristics to become increasingly complex, making them difficult to identify manually. Therefore, a machine learning-based approach is needed to classify player behavior more objectively and systematicallys. This study aims to classify Roblox player behavior into four categories, namely active, casual, social, and creative players, using the Naïve Bayes algorithm. This algorithm was chosen because it has a simple and efficient computational process and is suitable for questionnaire data that has been transformed into numerical form. A total of 523 responses were successfully collected, and after the selection and preprocessing stages, 520 data points were obtained to be used as the research dataset. The data were processed through data cleaning, encoding, missing value handling, and dataset splitting using an 80% training data and 20% test data. The results showed that the model achieved an accuracy of 62.5%. Evaluation using precision, recall, and F1-score metrics revealed that The results showed that the model produced an accuracy of 62.5%, with a precision value of 63%, recall of 62%, and F1-score of 62%. Although the accuracy obtained is not yet high, these results indicate that the Naïve Bayes algorithm can be used as a baseline in classifying player behavior based on questionnaire data that has subjective and complex characteristics. The his study contributes by providing a baseline classification model for Roblox player behavior based on questionnaire data, along with insights into player characteristics that can serve as a reference for developers in understanding user behavior, thereby supporting the development of more adaptive features that better align with players' needs.