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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) International Journal of Advances in Data and Information Systems Jumat Informatika: Jurnal Pengabdian Masyarakat MDP Student Conference Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Riset Sistem dan Teknologi Informasi
Bella Okta Sari Miranda
Master Program Of Informatics, Universitas Ahmad Dahlan, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Journal : MDP Student Conference

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Understanding Generation Z’s Mental Health in Relation to Working Hours: A TikTok Sentiment Analysis Approach Putri, Isyiffah Falujjah Anugrah; Rahmatika, Alfilia Hilda; Miranda, Bella Okta Sari; Adiyana, Imam
MDP Student Conference Vol 5 No 1 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i1.15335

Abstract

In the digital work environment, long working hours and blurred work-life boundaries have become increasingly common, raising concerns about employees’ mental health, particularly among Generation Z. This study examines how Generation Z perceives working hours and their impact on mental health using social media–based sentiment analysis. Data were collected from 848 TikTok videos posted between 2023 and 2025, consisting of captions and comments related to work-related stress. Sentiment classification was performed using machine learning models, namely Multinomial Naïve Bayes, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). Text representation employed Bag-of-Words and Term Frequency–Inverse Document Frequency (TF-IDF). The results indicate that positive and negative sentiments dominate discussions, reflecting strong emotional responses to working-hour pressure, while neutral sentiment remains difficult to identify. Among the evaluated models, SVM with a radial basis function kernel achieved the best overall performance. These findings highlight the potential of TikTok based sentiment analysis for understanding Generation Z’s mental health in digital work contexts.
Co-Authors Adiyana, Imam Annafii, Moch. Nasheh Anton Yudhana Dianda Rifaldi Fanani, Galih Pramuja Inngam Fauzan Purma Ramadhan Herman Yuliansyah Herman Yuliansyah, Herman Imam Adiyana Iriene Putri Mulyadi Khoirul Anam Dahlan Moch. Nasheh Annafii Muhammad Eka Purbaya Muhammad Kunta Biddinika Muhammad Kunta Biddinika Novita Damayanti Putri, Isyiffah Falujjah Anugrah Rahmatika, Alfilia Hilda Rusydi Umar Tri Stiyo Famuji Vanji Saputra6