Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction

  • Gege Ardiyansyah Universitas Bina Darma, Indonesia
  • Ferdiansyah Ferdiansyah Universitas Bina Darma, Indonesia
  • Usman Ependi Universitas Bina Darma, Indonesia
Keywords: Prediction, Deep Learning, Cryptocurrency, Website, Regression


Cryptocurrency is a digital asset designed by cryptography, such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). Cryptocurrency uses Blockchain technology to ensure security, transparency, ease of locating, and unchangeability. This makes cryptocurrency very popular in many sectors, especially in the financial industry. Although, the uncertainty and the dynamic change of cryptocurrency price make the risk for investment in this digital asset high. This is the reason why studies about cryptocurrency price prediction became popular globally. This study intended to predict cryptocurrency prices using hybrid GRU LSTM than setting up the epoch to get the most accurate prediction model. The researcher would make a web-based application that can be used by the public, especially those involved in cryptocurrency investment. The result was a web-based application that could predict the price of cryptocurrency for the next few days, which had been validated using data from the previous 7 days, 14 days, 30 days, 60 days, and 90 days.


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F. Ferdiansyah, S. H. Othman, R. Z. M. Radzi, and D. Stiawan, ‘A study of economic value estimation on cryptocurrency value back by gold, methods, techniques, and tools’, Journal of Information Systems and Informatics, vol. 1, no. 2, pp. 178–192, 2019.

W. Hastomo and A. S. B. Karno, ‘Kemampuan Long Short Term Memory Machine Learning Dalam Proyeksi Saham Bank BRI TBK’, Prosiding SeNTIK, vol. 4, no. 1, pp. 229–236, 2020.

N. F. B. Pradana and S. Lestanti, ‘Aplikasi Prediksi Jangka Pendek Harga Bitcoin Menggunakan Metode Arima’, Jurnal Ilmiah Informatika Komputer, vol. 25, no. 3, pp. 160–174, 2021.

M. M. Hasan, ‘Prediksi jangka pendek harga bitcoin menggunakan metode ARIMA’, 2019.

M. S. Islam and E. Hossain, ‘Foreign exchange currency rate prediction using a GRU-LSTM Hybrid Network’, Soft Computing Letters, p. 100009, 2020.

S. McNally, J. Roche, and S. Caton, ‘Predicting the price of bitcoin using machine learning’, in 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 2018, pp. 339–343.

R. Mittal, S. Arora, and M. P. S. Bhatia, ‘Automated Cryptocurrencies Prices Prediction Using Machine Learning’, ICTACT Journal on Soft Computing, vol. 08, no. 04, p. 4, 2018.

D. R. Pant, P. Neupane, A. Poudel, A. K. Pokhrel, and B. K. Lama, ‘Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis’, in 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, Oct. 2018, pp. 128–132. doi: 10.1109/CCCS.2018.8586824.

C.-H. Wu, C.-C. Lu, Y.-F. Ma, and R.-S. Lu, ‘A new forecasting framework for bitcoin price with LSTM’, in 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018, pp. 168–175.

F. Ferdiansyah, S. H. Othman, R. Z. R. M. Radzi, D. Stiawan, Y. Sazaki, and U. Ependi, ‘A lstm-method for bitcoin price prediction: A case study yahoo finance stock market’, 2019, pp. 206–210.

A. D. Arisandi, L. Atika, E. S. Negara, and K. R. N. Wardani, ‘Prediksi Mata Uang Bitcoin Menggunakan LSTM Dan Sentiment Analisis Pada Sosial Media’, Jurnal Ilmiah KOMPUTASI, vol. 19, no. 4, pp. 559–566, 2020.

R. Albariqi and E. Winarko, ‘Prediction of Bitcoin Price Change using Neural Networks’, in 2020 International Conference on Smart Technology and Applications (ICoSTA), Feb. 2020, pp. 1–4. doi: 10.1109/ICoSTA48221.2020.1570610936.

M. De Caux, F. Bernardini, and J. Viterbo, ‘Short-Term Forecasting in Bitcoin Time Series Using LSTM and GRU RNNs’, in Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020), Brasil, Oct. 2020, pp. 97–104. doi: 10.5753/kdmile.2020.11964.

Squark, ‘ROOT MEAN SQUARE ERROR OR RMSE’. (accessed Aug. 28, 2019).

N. Vandeput, ‘Forecast KPI: RMSE, MAE, MAPE & Bias’, 2019. (accessed Apr. 10, 2020).

B. Santoso, A. I. S. Azis, and others, Machine Learning & Reasoning Fuzzy Logic Algoritma, Manual, Matlab, & Rapid Miner. Deepublish, 2020.

N. J. Salkind, Encyclopedia of research design, vol. 1. Sage, 2010.

Z. Bobbitt, ‘What is Considered a Good RMSE Value?’, 2021. Accessed: Jan. 16, 2022. [Online]. Available:

V. Gaspersz, ‘Production planning and inventory control’, PT Gramedia Pustaka Umum, Jakarta, 2004.

U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, ‘Forecasting error calculation with mean absolute deviation and mean absolute percentage error’, in Journal of Physics: Conference Series, 2017, vol. 930, no. 1, p. 12002.

C. D. Lewis, Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann, 1982.

S. U. Masruroh, S. Hanna, N. Azza, K. Kamarusdiana, and N. A. R. Vitalaya, ‘Klasifikasi Mazhab Menggunakan Metode Naïve Bayes (Studi Kasus: Salat)’, JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 8, no. 1, pp. 74–79.

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How to Cite
Ardiyansyah, G., Ferdiansyah, F., & Ependi, U. (2022). Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction. Journal of Information Systems and Informatics, 4(4), 958-974.