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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab METIK JURNAL Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma INOVTEK Polbeng - Seri Informatika
Edvin Ramadhan
Institut Teknologi Bandung
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Mathematics

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Computational Analysis of Student Stress on Social Media using Support Vector Machine and Latent Dirichlet Allocation Fauzan, Mochammad; Ashaury, Herdi; Ramadhan, Edvin
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8jcvxk45

Abstract

This study develops a two-stage machine-learning framework to identify academic stressors among Indonesian university students using Twitter data. A Support Vector Machine (SVM) classifier was trained on manually annotated tweets and benchmarked against Naïve Bayes, logistic regression, and random forest, achieving an accuracy of 0.91 and a macro F1-score of 0.914, outperforming all baselines. Tweets classified as stress-related with ≥75% confidence were subsequently analyzed using Latent Dirichlet Allocation (LDA), which generated six coherent stressor categories. The framework reveals both structural academic pressures and culturally specific patterns, including references to “dosen killer” and emerging mental-health vocabulary. Contributions include the first Indonesia-focused stressor map derived from large-scale social media discourse and the integration of confidence filtering to enhance topic quality. While results demonstrate the feasibility of social-media–based stress detection, limitations remain regarding temporal drift, annotation bias, and demographic representativeness. Future research should incorporate real-time streaming pipelines, multimodal annotation, and longitudinal evaluation to enhance robustness and early-warning potential.
Co-Authors Ashaury, Herdi Bambang Riyanto Trilaksono Fahrezi Ahmad, Rafly Fikri Fajar Firdaus, Fajar Fauzan, Mochammad Firdaus, Muhammad Fauzi Gunawan Abdillah, Gunawan Hadi Sakaro, Dwi Wahyu Kuncoro Hadiana, Asep Id Iping Supriana Suwardi Julianthy, Denissya Kharisma S, Moh Iqbal Kiki Hidayat, Kiki Nabiel Syahreza, Fabian Nurul Sabrina, Puspita Puspita Nurul Sabrina, Puspita Nurul Rahmah, Revina Nur Shabrina, Puspita Nurul Wahidin, Farhan Raihan Wina Witanti