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

Authors

  • Gege Ardiyansyah Universitas Bina Darma, Indonesia
  • Ferdiansyah Ferdiansyah Universitas Bina Darma, Indonesia
  • Usman Ependi Universitas Bina Darma, Indonesia
Pages Icon

DOI:

https://doi.org/10.51519/journalisi.v4i4.365

Keywords:

Prediction, Deep Learning, Cryptocurrency, Website, Regression

Abstract

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.

Downloads

Download data is not yet available.

References

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’. https://squarkai.com/root-mean-square-error-or-rmse/#.XWVCLegzZPY (accessed Aug. 28, 2019).

N. Vandeput, ‘Forecast KPI: RMSE, MAE, MAPE & Bias’, 2019. https://medium.com/analytics-vidhya/forecast-kpi-rmse-mae-mape-bias-cdc5703d242d (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: https://www.statology.org/what-is-a-good-rmse/

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.

Downloads

Published

2022-11-16

Issue

Vol. 4 No. 4 (2022): Journal of Information Systems and Informatics

Section

Articles

License

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
G. Ardiyansyah, F. Ferdiansyah, and U. Ependi, “Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction”, journalisi, vol. 4, no. 4, pp. 958–974, Nov. 2022, doi: 10.51519/journalisi.v4i4.365.

Most read articles by the same author(s)

<< < 1 2