IMPLEMENTATION OF A WEB-BASED DECISION TREE MODEL FOR CLASSIFICATION OF MOBILE GAME ADDICTION POTENTIAL BASED ON DURATION, FREQUENCY, AND SLEEP PATTERN

Abstract

The rapid growth of mobile gaming has increased playing duration and frequency among users, potentially affecting sleep patterns and leading to addictive behavior. Early identification of mobile game addiction is important to help users recognize unhealthy gaming habits. This study aims to develop a web-based classification system for detecting mobile game addiction potential using the Decision Tree (C4.5) algorithm. The classification process is based on three indicators: playing duration, playing frequency, and sleep patterns. A dataset consisting of 19 active mobile game users was collected and processed using entropy and information gain calculations to generate classification rules and construct a decision tree model. The system classifies users into three categories: mild, moderate, and severe addiction potential. The results showed that Playing Duration produced the highest Information Gain value (1.038) and was selected as the root node of the decision tree. System testing demonstrated that all evaluated testing samples were classified correctly, resulting in an accuracy of 100% for the tested samples. The developed web-based system provides clear and interpretable classification results and can support the early identification of mobile game addiction potential based on user gaming behavior.