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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Prosiding SNATIF ANDHARUPA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Eduvest - Journal of Universal Studies Community: Jurnal Pengabdian Pada Masyarakat Scientific Journal of Informatics
Nova Rijati
Teknik Informatika Universitas Dian Nuswantoro
Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Neuroscience Social Sciences Other

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Journal : Scientific Journal of Informatics

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Development of a Mental Health Classifier Using LSTM and Text Preprocessing Techniques Haryoko, Priyo; Syukur, Abdul; Rijati, Nova
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21216

Abstract

Purpose: This study aims to address undiagnosed mental health conditions using social media for early detection. By applying advanced preprocessing techniques and LSTM models, the research improves classification accuracy for depression and PTSD. It highlights deep learning’s potential to process unstructured data and provides a scalable solution for real-world mental health monitoring. Methods: Data was collected from Twitter using keywords like "depression" and "anxiety." Preprocessing included normalization, tokenization, stemming, and stopword removal. An LSTM-based model with GloVe embeddings, LSTM layers, and dropout was developed. The model’s performance was evaluated using metrics like accuracy, precision, recall, and F1-score to ensure robust and applicable results. Result: The LSTM model achieved 90% accuracy, outperforming Random Forest (89%) and SVM (89%). Preprocessing steps like tokenization and stemming boosted performance by 15%. The model effectively captured temporal dependencies in text, showcasing its ability to analyze unstructured social media content for mental health detection. Novelty: This study integrates advanced text preprocessing with LSTM to enhance mental health detection. Unlike traditional methods, it captures temporal nuances using GloVe embeddings. The scalable framework provides a reliable solution for real-world applications, paving the way for multilingual and cross-platform research in mental health analytics.
Co-Authors Abdul Syukur Ajib Susanto Aqqad, Muslihul Aris Marjuni Astuti, Yani Parti Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Desi Purwanti Kusumaningrum Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Edi Sugiarto Etika Kartikadarma Haryoko, Priyo Heribertus Himawan Ifan Rizqa Jatmoko , Cahaya Marjuni, Aris Pratama, Ahmad Sabil Deva Pratama, Yudha Hady Pratiwi, Vila Rusantia Pujiono Pujiono Puri Sulistiyawati Purwanto Purwanto Purwanto, Purwanto Rahmasari, Erisa Adyati Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Sugiyanto - Toto Haryadi, Toto Wicaksana, Endra Maulia Yaacob, Noorayisahbe Mohd. Yuliman Purwanto Yuliman Purwanto Zainal Arifin Hasibuan