Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 17, 2025bit-TechOpen Access

Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit

View Full Paper
Ask AI
Bookmark
Share

Authors

ATAlfiyan Tegar Budi Satria TegarHHHerliyani HasanahIOIntan Oktaviani

Discussion

Loading...

Member takes

Overview

Machine learning predicts addiction levels in students using interactive visualizations, highlighting its implications for mental health.

Key Points

  • The model predicts social media addiction, achieving an impressive R² value of 0.9903, indicating high accuracy.
  • Key metrics include a low Mean Absolute Error of 0.0370, showcasing its reliability as a predictive tool.
  • The approach utilizes a Streamlit-based application with Random Forest for real-time predictions, combining various indicators.
  • This innovative method may enable timely interventions for students at risk of social media addiction, emphasizing its significance.

Cite This Study

Tegar et al. (2025) studied this question.

synapsesocial.com/papers/68c2368eb210217d64775026https://doi.org/10.32877/bt.v8i1.2715
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning model for prediction of smartphone addiction2025
  2. 2Predicting Online Gaming Engagement Levels Using Machine Learning Models2025
  3. 3A Hybrid Machine Learning Framework For Analyzing The Impact Of Social Media On Students’ Academic Performance2025
  4. 4Social media addiction: A comprehensive state of mental health2025
  5. 5Explainable machine learning for mental health prediction from social media behavior: a nested cross-validation study with SHAP and LIME interpretability2026