Classification of Instagram and TikTok Addiction Levels among University Students Using the Naive Bayes Classifier

Abstract

The widespread use of gadgets and internet connectivity has become an essential aspect of daily life, especially through intensive interaction with social media platforms. Excessive usage can lead to addictive behaviors that disrupt students’ academic productivity and concentration. Although research on social media addiction continues to grow, few studies specifically examine platform-level addiction (Instagram vs. TikTok) using multi-class classification approaches. Therefore, this study aims to assess the level of social media addiction among university students, focusing on users of Instagram and TikTok at Telkom University Purwokerto. The analysis employs the Naive Bayes Classifier algorithm using data collected from 100 respondents. Model performance is evaluated through a multi-class confusion matrix to compute accuracy, precision, recall, and F1-score. Separate datasets for Instagram and TikTok are used to enable platform-specific behavioral assessment. The results show that the Naive Bayes Classifier achieves strong performance, with 93% accuracy for the Instagram dataset and 90% for the TikTok dataset. Precision scores reach 95% and 91%, recall values 93% and 90%, and F1-scores 93% and 90%, respectively. These findings confirm that Naive Bayes is effective for classifying students’ levels of social media addiction. Overall, this research contributes a reliable machine-learning–based approach for evaluating digital behavior and provides insights for early detection, enabling universities to design targeted interventions for students at risk of problematic usage. The methodology may also be extended to analyze engagement patterns on emerging social media platforms in future studies.

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