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All Journal JIEET (Journal of Information Engineering and Educational Technology) Journal of Informatics and Computer Science (JINACS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Nafisah, Nurun
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Pengembangan Website Pergudangan berbasis FIFO untuk Optimalisasi Persediaan Barang di LMI Pusat Surabaya Nafisah, Nurun; Yamasari, Yuni
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 04 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n04.p635-645

Abstract

Hybrid Autoencoder Architectures with LSTM and GRU Layers for Bitcoin Price Prediction Yamasari, Yuni; Nafisah, Nurun; Yohannes, Ervin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.132

Abstract

The high volatility of cryptocurrency markets, particularly Bitcoin, poses significant challenges for accurate price forecasting. To address this issue, this study evaluates the performance of four autoencoder-based deep learning architectures: AE-LSTM, AE-GRU, AE-LSTM-GRU, and AE-GRU-LSTM. The models were developed and tested using a univariate approach, where only the closing price was used as input, and two different window sizes (30 and 60) were applied to analyse the effect of historical sequence length on prediction accuracy. Several parameter configurations, including the number of epochs, dropout rate, and learning rate, were explored to determine the optimal model performance. The dataset comprises Bitcoin’s daily closing prices from 2018 to 2025, encompassing diverse market phases, including both bullish and bearish trends. Model performance was assessed using four evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). The results indicate that the AE-LSTM-GRU consistently achieved the best overall performance across all configurations. For a window size of 30, it achieved an RMSE of 1.53067 and a MAPE of 1.98%, while for a window size of 60, the best performance recorded was an RMSE of 1.55217 and a MAPE of 2.09%. The hybrid structure combining LSTM’s capability to capture long-term dependencies with GRU’s efficiency in information decoding demonstrated strong robustness in modelling highly volatile time series. This study contributes to financial time series forecasting by presenting hybrid autoencoder architectures that strike a balance between predictive accuracy and computational efficiency, providing practical insights for researchers and practitioners in financial technology and cryptocurrency analytics
A Performance Comparison of LSTM and GRU Architectures for Forecasting Daily Bitcoin Price Volatility Nafisah, Nurun; Yamasari, Yuni; Yohannes, Ervin
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p156-167

Abstract

The highly volatile movement of Bitcoin prices necessitates the use of prediction methods capable of accurately capturing complex and rapidly changing patterns. This study aims to compare the performance of two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting Bitcoin prices based on historical time series data. The analysis was conducted using daily closing price data, with several parameter configurations applied, including dropout value, learning rate, and number of epochs at a window size of 30. The training process was carried out using a univariate approach to assess the fundamental ability of each model to learn temporal patterns without the influence of external variables. The results indicate that the GRU model consistently outperforms LSTM across most experimental settings. The best performance was achieved with 30 epochs, dropout 0.1, and a learning rate of 0.001, producing RMSE 1478.333, MAE 1000.900, R² 0.996081, and MAPE 1.973072. These metrics demonstrate a lower error level and a stronger fit to actual Bitcoin price movements. Moreover, a paired t-test confirmed that the performance gap between the two models is statistically significant. Overall, the findings suggest that the Gated Recurrent Unit architecture is more efficient in capturing nonlinear patterns and responding to the volatile dynamics of cryptocurrency price fluctuations, making it a promising approach for future predictive modeling in financial time series.
Co-Authors Ervin Yohannes, Ervin YUNI YAMASARI