An Analysis of the Impact of Social Media Addiction on Students’ Academic Performance Using K-Means and Decision Tree

Abstract

This study aims to analyze the relationship between social media addiction levels and students' academic performance. With the growing use of social media among university students, concerns have emerged regarding its potential negative impact on academic achievement. The data were obtained from the "Social Media Addiction vs Relationships" dataset and analyzed using two machine learning approaches: K-Means to classify groups based on usage hours and academic impact, and Decision Tree to predict academic satisfaction levels based on digital behavior. The findings reveal distinct clustering patterns that differentiate students based on their addiction levels and academic performance. The Decision Tree model achieved 100% accuracy on the test data in classifying the impact of social media use. These results highlight that daily usage hours and addiction scores are significant contributing factors. Based on these insights, the study recommends implementing digital intervention programs on campus to help mitigate the negative effects of social media addiction.