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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah FIFO JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) Petir Ensiklopedia of Journal Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pasopati : Pengabdian Masyarakat dan Inovasi Pengembangan Teknologi Jurnal Pengabdian Masyarakat Nasional
Bagus Priambodo
Universitas Mercu Buana
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Machine Learning Approaches to Sentiment Analysis of Mental Health Discussions on Platform X Yuwan Jumaryadi; Riri Fajriah; Umniy Salamah; Bagus Priambodo; Arie Lystha
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11350

Abstract

Sentiment analysis on mental health issues is crucial for understanding public perceptions of healthcare services. This study analyzed tweets related to mental health on platform X in 2025 using SVM, Random Forest, and Naive Bayes algorithms. Data was collected through web scraping with Python, then evaluated using a confusion matrix with accuracy, precision, f1-score, and recall metrics. The classification results showed a distribution of sentiment: positive (3,667 tweets), neutral (838 tweets), and negative (704 tweets). A comparative analysis of the three algorithms revealed that SVM achieved the highest accuracy (78.69%), followed by Random Forest (75.04%) and Naive Bayes (70.44%), proving the superiority of SVM in classifying mental health sentiment. These findings provide valuable insights for stakeholders in improving mental healthcare services based on public feedback, while also offering a reference for effective sentiment analysis methods for social media data.
Co-Authors Aditya Alif Afifah Fitri Anggraini Arie Lystha Ayaitulla Salsabilla Achmad Azlina Ahmad Delaila Rosa Wardiman Dewi Sri Utami Diky Firdaus Gushelmi Hana Prasetiyo Diennida Ika Nur Suciati Ilham Rahmaditia Arlingga Jumaryadi, Yuwan Misbahul Fajri Nazmi Aulia Putra Ardiansyah Rabiah Abdul Kadir Riri Fajriah Romeo Mulia Pratama Ruci Meiyanti Salamah, Umniy Samidi Samidi Vivie Herlina Yustika Erliani Zehandra Gibran Nugroho