Risqi Nur Avianti, Cucut Hariz Pratomo

Pola Perilaku Pemain Roblox: Pemodelan Klasifikasi Berbasis Naïve Bayes

Introduction

Pola perilaku pemain roblox: pemodelan klasifikasi berbasis naïve bayes . Klasifikasi perilaku pemain Roblox (aktif, kasual, sosial, kreatif) menggunakan algoritma Naïve Bayes. Model baseline ini menganalisis data kuesioner untuk memahami karakteristik pengguna.

Abstract

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.

Review

This study addresses the growing complexity of player behavior in online gaming platforms like Roblox, where manual identification of diverse activities (entertainment, social interaction, creativity) becomes increasingly challenging. Recognizing the need for an objective and systematic approach, the authors propose a machine learning-based solution to classify Roblox player behavior. The research aims to categorize players into four distinct types: active, casual, social, and creative, which is a commendable effort to bring structure to understanding user engagement on such a multifaceted platform. The motivation for this classification is clearly articulated, highlighting its potential to inform platform development and enhance user experience. The methodology employs the Naïve Bayes algorithm, justified by its computational efficiency and suitability for questionnaire data transformed into numerical form. A total of 523 responses were initially collected, refined to 520 data points after rigorous selection and preprocessing, which included data cleaning, encoding, and handling missing values. The dataset was then split into an 80% training set and a 20% test set, a standard practice for model development and evaluation. This systematic approach to data handling and model preparation suggests a robust foundational effort to build a reliable classification system. The results indicate that the Naïve Bayes model achieved an accuracy of 62.5%, with precision, recall, and F1-score values hovering around 62-63%. While the authors acknowledge that this accuracy is "not yet high," they appropriately frame it as a valuable baseline. This demonstrates that Naïve Bayes can serve as a starting point for classifying player behavior based on inherently subjective and complex questionnaire data. The study's main contribution lies in providing this foundational classification model and offering insights into player characteristics, which can be instrumental for developers seeking to understand user needs and create more adaptive features for the Roblox platform. Future work could build upon this baseline by exploring more advanced algorithms or feature engineering techniques to improve predictive performance.

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