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(Institut Informatika dan Bisnis Darmajaya, Indonesia)(2) * Faurani Santi Singagerda
(Institut Informatika dan Bisnis Darmajaya, Bandar Lampung, Indonesia)(3) Riko Herwanto
(Institut Informatika dan Bisnis Darmajaya, Bandar Lampung, Indonesia)*corresponding author
AbstractObjective of research to identify the application of LSTM on Ethereum predictions based on blockchain data and information. The study is an experimental study using the Long Short Term Memory (LSTM) method to predict blockchain information on the Ethereum market. The method is a development of Recurrent Neural Network (RNN) and Artificial Neural Network (ANN), and required several precise parameters to produce accurate predictions. The study analyzed several parameters such as the number of neurons in the hidden layer and the most appropriate max epoch to use. The results of the analysis show that using neurons 50 and max epoch 500 are able to predict ethereum prices using blockchain information well, seen from a very small error, namely MAPE of 1.69% with the highest price predictions occurring throughout the middle of 2021 as an effect of changes in the technology system, which is used which previously applied proof of work (mining) to proof of stake (validator) on the current ETH 2.0 technology, as a result there was a decrease in the supply of coins that was currently happeningKeywordsCryptocurrency, Fintech, Blockchain, Prediction, Merger
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DOIhttps://doi.org/10.29099/ijair.v6i1.429 |
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References
C. J. Coyne, M. C. Munger, G. J. Robson, and D. W. Thomas, “Cryptocurrency and the problem of intermediation,” JSTOR, 2016, Accessed: Jul. 08, 2022. [Online]. Available: https://www.jstor.org/stable/44000162.
S. Smetanin, A. Ometov, M. Komarov, P. Masek, and Y. Koucheryavy, “Blockchain Evaluation Approaches: State-of-the-Art and Future Perspective,” Sensors 2020, Vol. 20, Page 3358, vol. 20, no. 12, p. 3358, Jun. 2020, doi: 10.3390/S20123358.
D. K. Sharma, S. Pant, M. Sharma, and S. Brahmachari, “Cryptocurrency Mechanisms for Blockchains: Models, Characteristics, Challenges, and Applications,” Handb. Res. Blockchain Technol., pp. 323–348, Jan. 2020, doi: 10.1016/B978-0-12-819816-2.00013-7.
L. A. Abdillah, “Exploring Student’s Blended Learning through Social Media,” ComTech Comput. Math. Eng. Appl., vol. 7, no. 4, p. 245, Dec. 2016, doi: 10.21512/COMTECH.V7I4.2495.
S. Jani, “An Overview of Ethereum & Its Comparison with Bitcoin,” 2017, Accessed: Jul. 08, 2022. [Online]. Available: http://www.ijser.org.
L. Macedo, “Blockchain for trade facilitation: Ethereum, eWTP, COs and regulatory issues,” World Cust. J., vol. 12, no. 2, pp. 87–94, 2018, Accessed: Jul. 08, 2022. [Online]. Available: https://worldcustomsjournal.org/Archives/Volume 12%2C Number 2 (Sep 2018)/1855 00 WCJ v12n2 COMPLETE.pdf?_t=1563333690#page=94.
Luno, “Apa itu Ethereum? Mengenal Sejarah, Fungsi dan Cara Kerjanya - Luno,” https://discover.luno.com/id, 2020. https://discover.luno.com/id/apa-itu-ethereum-mengenal-sejarah-fungsi-dan-cara-kerjanya/ (accessed Jul. 08, 2022).
D. Vujičić, D. Jagodić, and S. Randić, “Blockchain technology, bitcoin, and Ethereum: A brief overview,” 2018 17th Int. Symp. INFOTEH-JAHORINA, INFOTEH 2018 - Proc., vol. 2018-January, pp. 1–6, Apr. 2018, doi: 10.1109/INFOTEH.2018.8345547.
S. Kumar and S. . Joshi, “Estimation of Load Forecast – 2020 Using Artificial Neural Network and Exploration,” Int. J. Res. Anal. Rev. , vol. 3, no. 2, pp. 48–53, 2016, Accessed: Jul. 08, 2022. [Online]. Available: http://ijrar.com/upload_issue/ijrar_issue_273.pdf.
A. Nilsen, B. D. H., and G. F. Hertono, “Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices,” AIP Conf. Proc., vol. 2391, Mar. 2022, doi: 10.1063/5.0073838.
A. F. Rasyid, D. Agushinta, and D. . Ediraras, “Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG),” Int. J. Comput. Inf. Technol., vol. 10, no. 5, 2021, Accessed: Jul. 09, 2022. [Online]. Available: https://192.99.73.25/index.php/ijcit/article/view/153.
M. Gao, J. Li, F. Hong, and D. Long, “Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM,” Energy, vol. 187, Nov. 2019, doi: 10.1016/J.ENERGY.2019.07.168.
C. Fan, J. Wang, W. Gang, and S. Li, “Assessment of deep recurrent neural network-based strategies for short-term building energy predictions,” Appl. Energy, vol. 236, pp. 700–710, Feb. 2019, doi: 10.1016/J.APENERGY.2018.12.004.
O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with deep learning : A systematic literature review: 2005–2019,” Appl. Soft Comput., vol. 90, p. 106181, May 2020, doi: 10.1016/J.ASOC.2020.106181.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/NECO.1997.9.8.1735.
A. Graves, Long Short-Term Memory. Springer, Berlin, Heidelberg, 2012.
J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism,” PLoS One, vol. 15, no. 1, pp. 1–15, Jan. 2020, doi: 10.1371/JOURNAL.PONE.0227222.
J. Zheng, C. Xu, Z. Zhang, and X. Li, “Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network,” May 2017, doi: 10.1109/CISS.2017.7926112.
H. Jang and J. Lee, “An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information,” IEEE Access, vol. 6, pp. 5427–5437, Nov. 2017, doi: 10.1109/ACCESS.2017.2779181.
F. Karim, S. Majumdar, H. Darabi, and S. Harford, “Multivariate LSTM-FCNs for time series classification,” Neural Networks, vol. 116, pp. 237–245, Aug. 2019, doi: 10.1016/J.NEUNET.2019.04.014.
C. Vercellis, Business intelligence: data mining and optimization for decision making. John Wiley & Sons, 2011.
Z. C. Lipton, D. C. Kale, and R. Wetzel, “Modeling Missing Data in Clinical Time Series with RNNs,” in Machine Learning for Healthcare , Jun. 2016, pp. 253–270, Accessed: Jul. 10, 2022. [Online]. Available: http://arxiv.org/abs/1606.04130.
Colah, “Understanding LSTM Networks,” 2016. http://colah.github.io/posts/2015-08-Understanding-LSTMs/ (accessed Jul. 10, 2022).
P. J. R and V. S. Das, “Cryptocurrency Price Prediction Using Long-Short Term Memory Model,” Int. J. Res. Sci. Innov. |, vol. V, no. 7, pp. 70–72, Jul. 2018, Accessed: Jul. 10, 2022. [Online]. Available: www.rsisinternational.org.
R. Mathur, T. Takavane, T. Shah, and R. Suryawanshi, “Stocks and Cryptocurrency Price Prediction using Long Short Term Memory,” Int. J. Eng. Res. Technol., vol. 11, no. 5, Jun. 2022, doi: 10.17577/IJERTV11IS050378.
D. Yi, J. Ahn, S. J.-A. Sciences, and undefined 2020, “An effective optimization method for machine learning based on ADAM,” mdpi.com, Accessed: Jul. 10, 2022. [Online]. Available: https://www.mdpi.com/633898.
Ferdiansyah, S. H. Othman, R. Zahilah Raja Md Radzi, D. Stiawan, Y. Sazaki, and U. Ependi, “A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market,” in ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding, Oct. 2019, pp. 206–210, doi: 10.1109/ICECOS47637.2019.8984499.
M. W. . Aldi, J. Jondri, and A. Annisa, “Analisis dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin,” 2018, Accessed: Jul. 10, 2022. [Online]. Available: https://core.ac.uk/download/pdf/299924171.pdf.
A. F. Khair, M. K. Awang, Z. A. Zakaraia, and M. Mazlan, “Daily streamflow prediction on time series forecasting,” J. Theor. Appl. Inf. Technol., vol. 95, no. 4, 2017, Accessed: Oct. 13, 2021. [Online]. Available: http://www.jatit.org/volumes/Vol95No4/8Vol95No4.pdf.
B. Huang, Q. Ding, G. Sun, and H. Li, “Stock prediction based on Bayesian-LSTM,” in ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Feb. 2018, pp. 128–133, doi: 10.1145/3195106.3195170.
Y. Chen, J. Wu, and Z. Wu, “China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach,” Expert Syst. Appl., vol. 202, p. 117370, Sep. 2022, doi: 10.1016/J.ESWA.2022.117370.
D. T. Campbell and J. C. Stanley, Experimental and quasi-experimental designs for research. Ravenio Books, 2015.
J. W. Creswell and J. D. Creswell, Research Design Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed. SAGE, 2018.
D. Pradiatiningtyas, C. B. Dewa, L. A. Safitri, and S. Kiswati, “The effect of satisfaction and loyalty towards digital payment system users among generation Z in Yogyakarta special region.,” 2020, doi: 10.1088/1742-6596/1641/1/012110.
W. M. A. Ahmed, “Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis,” North Am. J. Econ. Financ., vol. 57, p. 101379, Jul. 2021, doi: 10.1016/J.NAJEF.2021.101379.
Etherchain, “Ethereum (ETH) Mainnet - Charts - etherchain.org - 2022,” Etherum, 2022. https://etherchain.org/charts (accessed Mar. 18, 2022).
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, Oct. 2018, doi: 10.1016/J.EJOR.2017.11.054.
L. Troiano, E. M. Villa, and V. Loia, “Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications,” IEEE Trans. Ind. Informatics, vol. 14, no. 7, pp. 3226–3234, Jul. 2018, doi: 10.1109/TII.2018.2811377.
G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi: 10.1007/S10462-020-09838-1.
J. Truby, R. D. Brown, A. Dahdal, and I. Ibrahim, “Blockchain, climate damage, and death: Policy interventions to reduce the carbon emissions, mortality, and net-zero implications of non-fungible tokens and Bitcoin,” Energy Res. Soc. Sci., vol. 88, p. 102499, Jun. 2022, doi: 10.1016/J.ERSS.2022.102499.
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