Accuracy Comparison of Data Mining Methods for Internet Gaming Disorder Classification

Abstract

Excessive access to online games by players can lead to addiction. Online game addiction can be detected through various ways including self-report questionnaires. Based on the value of the answers in the questionnaire a person can be categorized into groups, mild addiction, moderate addiction and severe addiction. Generally, this assessment calculation is done manually or through procedural programming languages. This is not efficient for processing more and more data, for that the mechine learning classification model is applied to solve the problem of program code repetition. This study compared the performance of three mechine learning methods against two different types of questionnaires, namely questionnaires with Likert scale and questionnaires with yes no type. The case study used in this study is online game addiction among high school students in Banda Aceh City, Indonesia. This research successfully proved that the algorithm ... It is better to use questionnaires with data types...., while algorithms....are better to use for questionnaires with types.....with the accuracy of the three algorithms are as follows. This study reveals the emergence of online game addiction, especially among high school students within Banda Aceh city. The results depicted that as many as 6% of high school students in the city of Banda Aceh indicated experiencing online game addiction based on their reports. Another objective of this research is to find the best accuracy between Naive Bayes and Support vector machine (SVM) in classifying the severity of the online game. It found that SVM accuracy was higher than Naive Bayes for the case of online game addiction level classification in high school students in Banda Aceh. This study provided baseline data for further research.