[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122414-en":3,"doc-seo-122414-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122414,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit - Research Report","Rising social media usage among students creates growing concerns about mental health, academic outcomes, and interpersonal wellbeing. This study proposes a Streamlit-based web application that predicts social media addiction levels using the Random Forest algorithm. The model leverages daily usage hours, mental health scores, and conflicts attributed to social media, combining machine learning with interactive visualizations for real-time screening. Results indicate strong predictive performance beyond linear regression, with R²=0.9903 and low MAE, MSE, and RMSE values, supported by black-box testing.","Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit  \nAlfiyan Tegar Budi Satria1)*, Herliyani Hasanah2), Intan Oktaviani3)  \n1)2)3) Sistem Informasi, Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta, Jl. Bhayangkara No.55, Tipes, Kec. Serengan, Kota Surakarta, Jawa Tengah 57154  \n1)[tegarsh1@gmail.com](tegarsh1@gmail.com)  \n2)[herliyani_hasanah@udb.ac.id](herliyani_hasanah@udb.ac.id)  \n3)[intan_oktaviani@udb.ac.id](intan_oktaviani@udb.ac.id)  \nArticle history:  \nReceived 26 June 2025;  \nRevised 30 June 2025;  \nAccepted 01 July 2025;  \nAvailable online 10 August 2025  \nKeywords:  \nMachine Learning Prediksi  \nRandom Forest  \nSocial Media Addiction Streamlit  \nAbstract  \nThe increasing use of social media among students has raised concerns regarding its impact on mental health, academic performance, and interpersonal relationships. This study introduces a Streamlit-based web application that predicts social media addiction levels using the Random Forest algorithm. The model incorporates variables such as daily usage hours, mental health scores, and conflicts caused by social media. The innovation of this approach lies in combining machine learning with interactive visualizations for real-time addiction prediction, providing a user-friendly, data-driven tool for early screening. Unlike traditional models that primarily rely on self-reported data or simple metrics, this method integrates multiple behavioral and psychological indicators to improve prediction accuracy. The model outperforms linear regression in all key metrics, achieving an R² value of 0.9903, which explains 99.03% of the variation in addiction scores. It also reports a low Mean Absolute Error (MAE) of 0.0370, Mean Squared Error (MSE) of 0.0244, and Root Mean Squared Error (RMSE) of 0.1561, highlighting its accuracy. Black-box testing showed an average error of just 0.354% in predictions and confirmed that the app’s features function effectively across devices. These findings emphasize the potential of this application as an effective tool for identifying students at risk of social media addiction, enabling timely interventions, and offering a foundation for future improvements through real-time data integration and advanced machine learning models.  \nI. INTRODUCTION  \nThe rapid increase in social media usage among adolescents and students has raised significant concerns about its impact on mental health, academic performance, and interpersonal relationships. One of the most notable effects of excessive social media use is its influence on sleep quality and overall well-being. Studies have indicated that excessive social media use negatively affects sleep patterns in young people. For example, students with high levels of social media addiction tend to have poor sleep quality, characterized by shorter sleep duration and difficulty falling asleep [1] . Adolescents spending more than five hours per day on social media are more susceptible to insomnia symptoms [2] . Prolonged exposure to social media, combined with stress, has also been shown to significantly affect sleep quality among teenagers [3] .  \nThese findings are consistent with a growing body of research on the relationship between social media use and disrupted sleep. Intensive social media use correlates with disturbed sleep patterns, especially among highschool students [4] . Additionally, compulsive social media behavior contributes to stress and anxiety, which in turn exacerbates sleep disorders among students [5] . Further, social media activity late at night has been linked to poor sleep quality and emotional instability among students [6] . These findings underscore the importance of addressing social media addiction to prevent negative effects on health and academic performance.  \nHowever, while the negative effects of social media on sleep are well-documented, existing research often relies on self-reported data, which can be biased or inaccur","cbCaieryg7u3IpAP","https://ap.wps.com/l/cbCaieryg7u3IpAP","pdf",683708,1,11,"English","en",105,"# Introduction\n## Background and Impact on Sleep and Well-being\n## Research Gap in Objective, Data-Driven Prediction\n## Proposed Solution with Random Forest and Streamlit\n## Contribution of Interactive Visualization","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the negative effects of excessive social media use, especially regarding mental health, sleep quality, academic performance, and interpersonal relationships.\"},{\"question\":\"How does the proposed system predict social media addiction?\",\"answer\":\"It uses a Streamlit-based web application built with the Random Forest algorithm, incorporating variables such as daily usage hours, mental health scores, and social-media-related conflicts.\"},{\"question\":\"Why is the interactive Streamlit visualization important?\",\"answer\":\"Streamlit enables a user-friendly interface and interactive visualizations so the tool can provide real-time addiction predictions and support early screening for timely intervention.\"}]","Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit - 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