Perbandingan Kinerja Model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Prediksi Harga Cryptocurrency
-
Hanif Alhakim
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Aditia Yudhistira Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
Extreme volatility in the cryptocurrency market poses substantial financial risks, necessitating precision forecasting systems. The limitations of conventional statistical models in capturing non-linear dynamics have prompted the adoption of deep learning approaches. This study evaluates the comparative performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in predicting the closing prices of Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). Experiments utilized historical data from January 2023 to April 2026, partitioned with an 80:20 train-test ratio using a 60-day sliding window sequence. Dropout-based regularization and Early Stopping were implemented to prevent overfitting. As an empirical contribution, this research univariately examines the trade-off between memory cell complexity (LSTM) and architectural efficiency (GRU) on high-volatility data. The results demonstrate that GRU consistently outperforms LSTM across all instruments, reducing the Mean Absolute Percentage Error (MAPE) to a range of 2.93%-4.89%. Regarding computational efficiency, the GRU architecture reduced training duration by 13.33% to 36.92% compared to LSTM. Practically, these findings recommend GRU as an effective and efficient algorithmic foundation for algorithmic trading systems and digital portfolio risk management.
Downloads
References
R. Kaur, M. Uppal, D. Gupta, and S. Juneja, “Development of a cryptocurrency price prediction model : leveraging GRU and LSTM for Bitcoin , Litecoin and Ethereum,” PeerJ Comput. Sci., vol. 11, no. e2675, pp. 1–30, 2025, doi: 10.7717/peerj-cs.2675.
A. GIRSANG and STANLEY, “Cryptocurrency Price Prediction Based Social Network Sentiment Analysis Using LSTM-GRU and FinBERT,” IEEE Access, vol. 11, pp. 120530–120540, 2023, doi: 10.1109/ACCESS.2023.3324535.
S. Hansun, A. Wicaksana, and A. Q. M. Khaliq, “Multivariate cryptocurrency prediction : comparative analysis of three recurrent neural networks approaches,” J. Big Data, vol. 9, no. 1, p. 15, 2022, doi: 10.1186/s40537-022-00601-7.
A. Aljadani, “DLCP 2 F : a DL ‑ based cryptocurrency price prediction framework,” Discov. Artif. Intell., vol. 2, no. 1, p. 20, 2022, doi: 10.1007/s44163-022-00036-2.
S. Syed, A. I. Member, W. Mehmood, and Z. Syed, “Split-Second Cryptocurrency Forecast Using Prognostic Deep Learning Algorithms : Data Curation by Deephaven,” IEEE Access, vol. 11, pp. 128644–128654, 2023, doi: 10.1109/ACCESS.2023.3331652.
F. Rodrigues, “High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models : A Comparative Study,” MDPI, vol. 16, no. 4, pp. 1–21, 2025, doi: 10.3390/info16040300.
K. Koszewski, S. Mazumdar, and A. S. Kumar, “Understanding rate of return dynamics of cryptocurrencies : an experimental campaign,” Artif. Intell. Rev., vol. 57, no. 1, p. 29, 2024, doi: 10.1007/s10462-023-10629-7.
D. Perera, J. Lim, S. Gunraku, and W. H. Lim, “Cryptocurrency Analysis : Price Prediction of Cryptocurrency Using User Sentiments and Quantitative Data,” MONASH Univ., vol. 3, pp. 210–217, 2024, doi: 10.5220/0012315100003636.
P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, “Sentiment-driven cryptocurrency forecasting: analyzing LSTM, GRU, Bi-LSTM, and temporal attention model (TAM),” Soc. Netw. Anal. Min., vol. 15, no. 1, p. 27, May 2025, doi: 10.1007/s13278-025-01463-6.
M. Zubair et al., “An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction With Sentimental Cautioning,” IEEE Access, vol. 12, pp. 51395–51418, 2024, doi: 10.1109/ACCESS.2024.3367129.
H. Nuha, “Mean Squared Error (MSE) dan Penggunaannya,” JAPATUM, vol. 3, no. 4, pp. 24–26, 2025, doi: 10.59328/ /JAPATUM.2024.3.4.107.
T. R. Mahesh et al., “Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 14, 2024, doi: 10.1007/s44196-023-00397-1.
D. I. Puteri, “Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah,” Euler J. Ilm. Mat. Sains dan Teknol., vol. 11, no. 1, pp. 35–43, 2023, doi: 10.34312/euler.v11i1.19791.
A. A. Suryanto and A. Muqtadir, “Penerapan Metode Mean Absolute Error (MAE) dalam Algoritme Regresi Linier untuk Prediksi Produksi Padi,” SAINTEKBU J. Sains dan Teknol., vol. 11, no. 1, p. 78, 2019, doi: 10.32764/saintekbu.v11i1.298.
H. H. Nuha, “Mean Absolute Percentage Error (Mape) dan Penggunaannya,” JAPATUM, vol. 3, no. 4, pp. 26–29, 2025, doi: 10.59328/JAPATUM.2024.3.4.108.
N. Yudistira et al., Prediksi Deret Waktu Menggunakan Deep Learning. Malang: Universitas Brawijaya Press, 2023. [Online]. Available: https://books.google.co.id/books?id=pOLXEAAAQBAJ
T. Kuncara and K. P. Anugrah, “Analisis Volatilitas Cryptocurrency Pada Sebelum Pandemi Dan Pada Saat Pandemi Covid 19 Dengan Metode Return Pada Bitcoin Dan Ethereum,” Keunis, vol. 11, no. 1, pp. 86–94, 2023, doi: 10.32497/keunis.v11i1.3981.
D. M. U. Atmaja and A. R. Hakim, “Peramalan Harga Mata Uang Kripto Solana Menggunakan Metode Support Vector Regression (Svr),” J. Media Elektro, vol. 10, no. 2, pp. 97–104, 2022, doi: 10.35508/jme.v0i0.8117.
N. Saptadi, H. Kristiawan, A. Nugroho, N. Rahayu, Suwarmiyati, and B. Waseso, DEEP LEARNING Teori, Algoritma, dan Aplikasi. BANTEN: Sada Kurnia Pustaka, 2025. [Online]. Available: https://books.google.co.id/books?id=JQBLEQAAQBAJ
D. Lusiyanti, S. Musdalifah, A. Sahari, and I. Al Fajri, “Evaluasi Kinerja Algoritma Machine learning pada Dataset Skala Besar,” MathVision J. Mat., vol. 7, no. 1, pp. 84–92, 2025, doi: 10.55719/mv.v7i1.1661.
Y. Perdana, N. R. Hanum, A. Rabiula, and Y. Anzari, “Analisis Perbandingan Model Gru Dan Lstm Untuk,” J. Akad., vol. 17, no. 2, pp. 54–60, 2025, doi: 10.53564/akademika.v17i2.1692.
P. B. Wintoro, H. Hermawan, M. A. Muda, and Y. Mulyani, “Implementasi Long Short-Term Memory pada Chatbot Informasi Akademik Teknik Informatika Unila,” Expert J. Manaj. Sist. Inf. dan Teknol., vol. 12, no. 1, p. 68, 2022, doi: 10.36448/expert.v12i1.2593.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Kinerja Model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Prediksi Harga Cryptocurrency
Pages: 342-352
Copyright (c) 2026 Hanif Alhakim, Aditia Yudhistira
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
