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Soeleman, M. Arief
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Aerospace Engineering Automotive Engineering 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 Mathematics Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Suicidal Ideation Detection in Social Media using Optimized CNN-BiLSTM Architecture Putri Novitasari, Hestiana; Soeleman, M. Arief; Rosita Sari, Sifa Ayu; Maida, Mamay
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11926

Abstract

This research aims to develop an optimized hybrid deep learning model for detecting suicidal ideation from social media text. The growing volume of online discussions, particularly on platforms such as Reddit, provides valuable signals for early identification of individuals at risk; however, the linguistic characteristics of user-generated content are highly diverse and often noisy. To address this challenge, this study proposes an Optimized CNN-BiLSTM architecture enhanced with a dropout rate of 0.6 and a strategic training approach utilizing Early Stopping (patience=3) and a Learning Rate Scheduler (ReduceLROnPlateau) to prevent local minima and ensure convergence stability. The dataset used consists of 232,074 text entries with a balanced class distribution (50% suicide, 50% non-suicide) to ensure the validity of evaluation metrics and eliminate majority class bias. Experimental results demonstrate that the optimized model achieves an accuracy of 94.96%, precision of 95.70%, recall of 94.15%, and an F1-score of 94.92%, indicating a significant improvement over the baseline CNN-BiLSTM and single BiLSTM models. Furthermore, interpretability analysis via keyword visualization (Word Cloud) validates that the model effectively captures semantically relevant emotional expressions of despair. These findings suggest that the optimized hybrid architecture provides a robust and operationally viable approach for supporting real-time early-warning systems on social media platforms to facilitate timely mental health interventions.
Co-Authors Abdul Syukur Abi Senoprabowo Affandy Affandy Affandy Al zami, Farrikh ALI MUQODDAS Arie Nugroho, Arie Assaqty, Mohammad Iqbal Saryuddin Azhar, Saifulloh Casidi, Casidi Dliyauddin, Muhammad Elkaf Rahmawan Pramudya Guruh Fajar Shidik Maida, Mamay Marjuni, Aris Muslih - Muslih Muslih Nurhindarto, Aris Prasetya, Rudy Eko Pulung Nurtantio Andono Puri Sulistiyawati Putri Novitasari, Hestiana Rahmatullah, Muhammad Rifqi Fadhlan Rahmawan P., Elkaf Ricardus Anggi Pramunendar Rosita Sari, Sifa Ayu Sari Ayu Wulandari Supriyono Asfawi Triyotomo, Triyotomo Tutik Khotimah Widhiariandoko, Adhitya Zami , Farrikh Al