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Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier Solichin, Achmad; Zulqan, Helmi; Painem, Painem; Pradiptha, Anindya Putri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5357

Abstract

Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.
Co-Authors Abdullah 'Alim Abdurrohim Musthofa Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Zainul Mafakhir Akbar, Kafi Kurnia Alfredo Pasaribu Alhafiz, Muhammad Ihza Ananda Surya, Archie Andi Hakim Arif Andika Pratama Anggi Ayu Ningtyas Anindya Putri Pradiptha Arif, Andi Hakim Arip Hidayat, Asep Lukman Asmoro, Phaksi Bangun Bayu Raditya Nasution Chaerullah, Dhiesky Chalid, Iqbal Chandra, Joko Christian Dasril Aldo Dedy Mirwansyah Desena, Wahyu Desiawan, Masdar Dewantara, Erno Kurniawan Dwi Kristanto Dwi Kristanto Emil Salim Fadlan Amrullah Fahrullah Fahrullah Galih Gumilar Widhasmara Goenawan Brotosaputro Hanafi, Mohammad Afif Hari Soetanto Hidayat, Asep Lukman Arip Ikhsan, Rifqi Dainur Irennada Ismail Adi Susanto Jody Khaeri Diniari Khansa Khairunnisa Kurnianta, Kristana Lia Amellia Putri Lutfi Nukman Majid, Muhammad Farras Masdar Desiawan Mochammad Andika Putra Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Agus Arianto Muhammad Ali Akbar Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Muharam, Asep Budiyana Nair, Anju A Nanda Arista Rizki Nariza Wanti Wulan Sari Nazori AZ Nita, Yulia Noor Ferdyansyah Nugroho, Ludi Nurwijayanti Obby Oktafianto Painem, Painem Pradana, Rizky Pradiptha, Anindya Putri Pramudita, Bagas Prayogi, Muhamad Nur Rahmat Kurniawan Rasyid, Annisa Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Richki Hardi Riki Wijaya Rizki Darmawan, Dika Robby Suganda Rusdah Rusdah Saddam, M Amiruddin Setiyadi, Prambudi Sister, Maya Gian Suherman Achmad Syahrul, Ahmad Tan Wee Chang Tetlageni, Muhamad Ridho Triyono, Gandung Tulodo, Bernadeta Asri Rejeki Ummu Habibah Romlah Utomo Budiyanto Wati, Lisna Wirasno, Wirasno Zainal A. Hasibuan Zulfikar Rosadi Zulqan, Helmi