Short-Term Cryptocurrency Price Prediction Using Bi-LSTM Method with Interactive Web
Abstract
Short-term Bitcoin price prediction is a crucial aspect of transaction decision-making, especially for investors. In this study, a Bidirectional Long Short-Term Memory (Bi-LSTM) model was developed for short-term Bitcoin price prediction. The Bidirectional LSTM is designed to capture temporal context in both directions, allowing the model to process information from past and future time steps simultaneously. The model was validated using real-world data, including Bitcoin stock price datasets. The results show that the model achieved high accuracy, with a Root Mean Square Error (RMSE) of 56.90 on the training data and 157.35 on the test data, along with a Mean Absolute Error (MAE) of 366.40 and 486.63, respectively. The Bidirectional Least Square Memory model accurately predicted Bitcoin prices over a specific time period. This application integrates the model into a web application, enabling users to obtain real-time Bitcoin price predictions through a user-friendly interface.
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References
A. S. Riyadi, I. P. Wardhani, Irfan, and A. Perdana, “Aplikasi Perbandingan Prediksi Harga Bitcoin Menggunakan Deep Learning Dengan Metode Arima, Sarima, Ltsm Dan Gradient Boosting Regressor,” Semin. Nas. Teknol. Inf. dan Komun. STI&K, vol. 7, no. 1, pp. 192–199, 2023.
S. Min, R. Song, and W. Zhu, “The 2021 Bitcoin Bubbles and Crashes – Detection and Classification,” SSRN Electron. J., 2021, doi: 10.2139/ssrn.3949166.
D. Deshwal, “Bitcoin Price Prediction Using Machine Learning,” INTERANTIONAL J. Sci. Res. Eng. Manag., vol. 06, no. 05, May 2022, doi: 10.55041/IJSREM13140.
H. Utama, “Pendekatan Deep Learning Menggunakan Metode Lstm Untuk Prediksi Harga Bitcoin,” Indones. J. Comput. Sci. Res., vol. 2, no. 2, pp. 43–50, Aug. 2023, doi: 10.59095/ijcsr.v2i2.77.
J. Zhang, L. Ye, and Y. Lai, “Stock Price Prediction Using CNN-BiLSTM-Attention Model,” Mathematics, vol. 11, no. 9, p. 1985, Apr. 2023, doi: 10.3390/math11091985.
S. J. Pipin, R. Purba, and H. Kurniawan, “Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 806–815, Aug. 2023, doi: 10.47065/josyc.v4i4.4014.
J. Julianto, “Analisis Investasi Dalam Memprediksi Pergerakan Harga Bitcoin Dengan Menggunakan Recurrent Neural Network Pada Platform Indodax,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 8, no. 2, p. 136, 2022, doi: 10.24014/rmsi.v8i2.17233.
A. Santoso, A. Irma Purnamasari, and Irfan Ali, “Prediksi Harga Beras Menggunakan Metode Recurrent Neural Network Dan Long Short-Term Memory,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 11, no. 1, pp. 128–136, 2024, doi: 10.30656/prosisko.v11i1.7921.
R. Roslidar, N. Brilianty, M. J. Alhamdi, C. N. Nurbadriani, E. Harnelly, and Z. Zulkarnain, “Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier,” Indones. J. Electr. Eng. Informatics, vol. 12, no. 1, pp. 40–52, 2024, doi: 10.52549/ijeei.v12i1.4732.
N. F. B. Pradana and S. Lestanti, “Aplikasi Prediksi Jangka Pendek Harga Bitcoin Menggunakan Metode Arima,” J. Ilm. Inform. Komput., vol. 25, no. 3, pp. 160–174, 2020, doi: 10.35760/ik.2020.v25i3.3128.
Sapardi, W. Hadikristanto, and N. T. Kurniadi, “Implementasi Pengembangan Aplikasi Sistem Manajemen Aset Berbasis Web Menggunakan Metode Waterfall Untuk Mengoptimalkan Penggunaan Aset Pada PT. Hutama Karya (Persero),” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 4, pp. 401–408, Oct. 2023, doi: 10.47233/jteksis.v5i4.948.
I. Nurhaida, M. Sobiri, and S. Jaya, “Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning,” JSAI (Journal Sci. Appl. Informatics), vol. 6, no. 2, pp. 197–204, Jun. 2023, doi: 10.36085/jsai.v6i2.5288.
R. Juwita, D. M. Ramadhani, and A. W. I. Maris, “The Determinants of Cryptocurrency Returns,” J. Ilmu Keuang. dan Perbank., vol. 12, no. 2, pp. 235–246, Jun. 2023, doi: 10.34010/jika.v12i2.9461.
Efrian, S. Defit, and Sumijan, “Prediksi Tingkat Kebutuhan Bandwidth Jangka Panjang Menggunakan Metode Algortima Backpropagation,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 4, no. 1, pp. 1–11, 2022, [Online]. Available: https://doi.org/10.47233/jteksis.v4i1.310
M. Hafizh, T. Novita, D. Guswandi, H. Syahputra, and L. Mayola, “Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 242–249, 2023, doi: 10.47233/jteksis.v5i3.847.
D. Jansen, T. Handhayani, and J. Hendryli, “Penerapan Metode Long Short-Term Memory Dalam Memprediksi Data Meteorologi Di Kalimantan Timur,” Simtek J. Sist. Inf. dan Tek. Komput., vol. 8, no. 2, pp. 348–352, Oct. 2023, doi: 10.51876/simtek.v8i2.202.
A. U. Muhammad, A. S. Yahaya, S. M. Kamal, J. M. Adam, W. I. Muhammad, and A. Elsafi, “A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), IEEE, Oct. 2020, pp. 1–6. doi: 10.1109/ICCIS49240.2020.9257651.
N. K. N. Nilasari, M. Sudarma, and N. Gunantara, “Prediksi Nilai Cryptocurrency Dengan Metode Bi-LSTM dan LSTM,” Maj. Ilm. Teknol. Elektro, vol. 22, no. 2, p. 221, 2023, doi: 10.24843/mite.2023.v22i02.p09.
Anas Fikri Hanif, Theopilus Bayu Sasongko, and Arif Dwi Laksito, “Perbandingan Kinerja LSTM, Bi-LSTM, dan GRU pada Klasifikasi Judul Berita Clickbait,” Indones. J. Comput. Sci., vol. 12, no. 4, pp. 2136–2150, 2023, doi: 10.33022/ijcs.v12i4.3281.
M. DIQI, HAMZAH, I. W. ORDIYASA, N. WIJAYA, and B. R. F. MARTIN, “Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction,” J. Buana Inform., vol. 15, no. 01, pp. 11–20, Apr. 2024, doi: 10.24002/jbi.v15i1.8707.
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