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All Journal POSITIF Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Komputasi dan Teknologi Informasi INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Innovative: Journal Of Social Science Research Majalah Cendekia Mengabdi Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics INSTALL: Information System and Technology Journal
Prastya, Septyan Eka
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Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest Febriani, Wulandari; Mambang, Mambang; Prastya, Septyan Eka; Sabella, Billy; Marleny, Finki Dona
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11415

Abstract

Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
Co-Authors Adla, Musyfia Ahmad Hidayat Ahmad Riki Renaldy Aulia, Hudatul Bayu Nugraha Bima Wicaksono Cipta, Subhan Panji Dodon Turianto Nugrahadi Ekawati, Norliana Fatma Indriani Fatmawati, Samsinah Fauzie Rahman Fawwaz, Nur Aisha Fawwaz, Nur Aisya Febriani, Wulandari Finki Dona Marleny Fitriani, Karlina Elreine Friska Abadi Haniffah Sri Rinjani Hidayaturrahman, Muhammad Hudatul Aulia Indah Wulandari Irwan Budiman Izhariaqi, Mahwa Kartika Kartika Kartika Kartika Mahmudah Mahmudah Mambang Maria Ulfah Maulida, Nur Meilianti Melda Melda Muhammad Itqan Mazdadi Muhammad Khairul Akbar Muhammad Reza Faisal, Muhammad Reza Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Zaini Bakri Muhammad Zulfadhilah Muliadi Nur Meilianti Maulida Nurhaeni Nurhaeni Nurhaeni Nurhaeni NURUL HIDAYAH Pebriadi, Muhammad Syahid Prasetya, M. Riko Anshori Pratama, Ramadhani Noor Putri Putri Putri Putri, Putri Putria, Evita Panca Rafi'i, Rafi'i Rahmini Rahmini Ripani, Ahmad Risma Maulida Risma Risma Risma Risma, Risma Rismawati Rismawati rizhariaqi, Mahwa Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari, Rudy Sabella, Billy Sandro Nesta Pembriano Subhan Panji Cipta Susanto, Jessika Kindly Tiara, Astia Rahma Yunida, Rahmi Yuslena Sari, Yuslena