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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Journal of Community Development
Emigawaty, Emigawaty
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Computer Science & IT Nursing Public Health Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Stock Price Prediction Using Deep Learning (LSTM) with a Recursive Approach Zakka, Muhamad Syukron; Emigawaty, Emigawaty
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10514

Abstract

Stock price prediction is a critical topic in financial technology research, as accurate forecasts support better decision-making in volatile markets. Numerous studies have applied statistical and machine learning models; however, most focus on one-step-ahead predictions and lack evaluation of recursive strategies in multi-day horizons. This study investigates the application of Long Short-Term Memory (LSTM) with a recursive forecasting approach to enhance stock price prediction accuracy. The dataset was enriched with multiple technical indicators and processed through a systematic Knowledge Discovery in Databases (KDD) pipeline, including preprocessing, transformation, modelling, and evaluation. Experimental results show that the recursive LSTM model achieves superior performance compared to baseline machine learning methods, with high accuracy in short-term horizons and stable performance up to nine days ahead, although accuracy gradually declines due to error accumulation. This work highlights the importance of integrating recursive forecasting with technical indicators to improve predictive capability in emerging markets and provides a foundation for developing adaptive financial forecasting frameworks.
Lightweight BiLSTM-Attention Model Using GloVe for Multi-Class Mental Health Classification on Reddit Branwen, Devin; Emigawaty, Emigawaty
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10157

Abstract

Mental health issues such as depression, stress, anxiety, and personality disorders are increasingly prevalent, particularly within online communities. This study proposes a lightweight and efficient multi-class classification framework to identify five mental health conditions using Reddit user-generated posts. While previous studies predominantly rely on conventional CNNs or standard machine learning techniques for binary classification, our work introduces a novel Bidirectional Long Short-Term Memory (BiLSTM) model integrated with an attention mechanism. The architecture is further enhanced by synonym-based data augmentation using the WordNet lexical database, which improves semantic diversity and enhances model robustness, particularly for underrepresented classes. Unlike prior works that focus narrowly on binary classification or employ transformer-based models with high computational demands, our model offers a lightweight, high-performance architecture optimized for multi-class detection and real-world deployment. Experimental results demonstrate that the proposed model achieves a peak validation accuracy of 95.02%, along with precision 95.08%, recall 95.02%, and F1-scores of 95.03%. These findings support the advancement of efficient AI-driven diagnostic systems in mental health analytics and lay the groundwork for future integration into mobile or resource-constrained platforms.
Co-Authors Branwen, Devin Maemunah, Mei Mulyatun, Sri NINIK TRI HARTANTI Nur’aini, Nur’aini Saptyawati, Laksmindra Sukmaningrum, Dinda Syafrianto, Syafrianto Wiji Nurastuti, Wiji Zakka, Muhamad Syukron