Indian Journal of Science and Technology

Abstract

Objectives: Internet Addiction Disorder (IAD) is one of the most widely social problems among the young college students. This study tried to evaluate and execute classification algorithms to be used for the analysis of internet addiction and related data. Methods: The survey had done with samples of 100 university students of various groups about internet addiction. These samples are classified using some classification algorithms such as Naive Bayes, JRip, ZeroR, J48, RepTree. Findings: We identified the people who are addicted in an internet using some classification algorithms. They are experimentally compared based on number of classified instances, time and error rate. To my dataset, Jrip concludes that 5 students are highly addicted, 65 students are moderately addicted and remaining 30 students have no internet addiction. JRip shows the best performance when compared to other four algorithms. Applications/Improvements: The Internet Addiction data set is analyzed and identifies particular internet addiction disorder affected in students and these are evaluated using brain images of students and identify the defects in the brain using image processing techniques. 

Keywords: Data Mining Classification Technique, Classification Algorithms, Internet Addiction Disorder