Implementation of The Naive Bayes Algorithm on Online Game Addiction and Its Impact on Students

Abstract

This research aims to implement the Naive Bayes algorithm in analyzing online game addiction and its impact on individuals. Online gaming addiction has become a global phenomenon with significant psychological, social, and academic implications. For this reason, an effective analytical tool is needed to identify the factors that contribute to this addiction and its impact. The Naive Bayes algorithm was chosen because of its ability to carry out classification based on probability, which is very suitable for handling complex and diverse data. This research collects data from questionnaires that cover demographic aspects, frequency of play, duration of play, and perceived impact. The analysis results show that the Naive Bayes algorithm has quite high accuracy in classifying individuals who are addicted to online games. In addition, this study identified several key factors that are closely related to addiction, such as age, gender, and motivation to play. The most prominent impacts of this addiction include decreased academic performance, disturbed sleep patterns, and problems with social relationships. With the implementation of the Naive Bayes algorithm, it is hoped that it can contribute to prevention and early intervention efforts against online game addiction. This research also opens up opportunities for further development in the use of other machine-learning techniques for digital behavior analysis.