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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Agus Tri Haryono, Agus Tri
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Computer Science & IT Electrical & Electronics Engineering

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Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study Haryono, Agus Tri; Sarno, Riyanarto; Sungkono, Kelly Rossa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp861-869

Abstract

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.
Attention-enhanced wasserstein GAN for agricultural market data imputation Shabrina, Ulima Inas; Sarno, Riyanarto; Anggraini, Ratih Nur Esti; Haryono, Agus Tri
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10549

Abstract

Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.
Co-Authors Kelly Rossa Sungkono Ratih Nur Esti Anggraini, Ratih Nur Esti Riyanarto Sarno Shabrina, Ulima Inas