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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Maida, Mamay
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Computer Science & IT

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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.
Improving the Accuracy of Obesity Classification Using a Stacking Classifier on Imbalanced Data with SMOTE Sari, Sifa; Soeleman, M.Arief; Maida, Mamay; Novitasari, Hestiana Putri
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.v10i1.11928

Abstract

Overweight continues to be a prevalent public health problem related to lifestyle behavior, eating behaviour and physical activity. The aim of this work is to develop a generalized and robust machine learning model having a high accuracy for categorizing obesity-level. The study applies to the Obesity Dataset with 1610 members and some preprocessing methods such selected data cleaning, categorical attributes transformation, train/test data set split and class imbalance under utilization of SMOTE approach. The modeling process is based on two base learners namely an optimized Random Forest and Gaussian Naïve Bayes that are fused by Stacking Classifier while using Logistic Regression as the meta-model. Experimental results show that the performance of stacking is the best where it obtains an accuracy rate of 86.34%, outperforming each single model. The analysis also reveals enhancements of various classification measures: stacking can indeed model complex non-linear dependencies between instances as well as simple linear ones. In general, the results serve to demonstrate that stacking-based ensemble learning is a strong solution for predicting obesity level and holds promise against early risk detection in preventive health care systems.
Co-Authors Novitasari, Hestiana Putri Putri Novitasari, Hestiana Rosita Sari, Sifa Ayu Sari, Sifa Soeleman, M. Arief Soeleman, M.Arief