ANALISIS INVESTASI DALAM MEMPREDIKSI PERGERAKAN HARGA BITCOIN DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK PADA PLATFORM INDODAX

Julianto Julianto

Abstract


Kemajuan teknologi yang semakin pesat membuat banyak bidang mengalami perubahan termasuk didalamnya bidang investasi asset digital terutama crypto. Ada banyak cara yang dilakukan oleh para trader maupun investor dalam melakukan perdagangan Bitcoin yang merupakan salah satu asset digital di dunia crypto. Indodax merupakan salah satu platform buatan local Indonesia yang melayani transaksi perdagangan asset digital. Analalisis teknikal dan fundamental dilakukan untuk memprediksi pergerakan harga bitcoin, namun volatilitas yang tinggi menyebabkan pergerakan bitcoin sulit untuk diprediksi. Penggunaan Reccurrent Neural Network yang merupakan sub bidang ilmu dari Machine Learning merupakan salah satu cara untuk dapat melakukan prediksi terhadap bitcoin.

Kata Kunci : RNN, LSTM, Bitcoin, Indodax, Training, Testing


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References


AGUIlar, Loyo J.S. (2017). Forecating volatility using Artificial Neural Networks and parametric methods. Dikutip 7 Agustus 2018, dari http://www.scriptiesonline.uba.uva.nl/document/654916

Ashrovy, Ron. (17 Oktober 2017). Recurrent Neural Network Par Four (END). Dikutip tanggal 22 Agustus 2019 dari https://medium.com/@ashrovy/recurrent-neural-network-part-4-d371474b8fa9

Aung, Sithu. (2019). Bitcoin Architecture Core. dikutip tanggal 23 Agustus 2019, dari https://id.pinterest.com/pin/551128073142441517/

Bengio, Y.; Courville, A.; Vincent, P. (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828.

Berry, M. and Linoff, G., (1999). Mastering Data Mining : The art and science of customer relationship management. John Wiley & Sons, Inc..

Berry, M.J. and Linoff, G.S., (2004). Data Mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.

Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.

Böhme, Rainer, et al. "Bitcoin: Economics, technology, and governance." Journal of Economic Perspectives 29.2 (2015): 213-38.

Cheng, Calvin (9 November 2017), “Overview of Bitcoin Architecture”, diakses tanggal 12 Agustus 2019, dari https://medium.com/@cloudycalvin/overview-of-bitcoin-architecture-cb3c88a1b20a

Bre, Facundo (2017, November). Prediction of wind pressure coefficients on building surfaces using Artificial Neural Networks. Dikutip tanggal 7 Agustus 2018, dari https://www.researchgate.net/publication/321259051_Prediction_of_wind_pressure_coefficients_on_building_surfaces_using_Artificial_Neural_Networks

Britz, Denny. (17 September 2015), “Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs”, diakses tanggal 7 agustus 2018, dari http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-RNNs/

Calvery, Jennifer Shasky (statement), Director Financial Crime Enforcement Network United States Departement of the Treasury Before the United States Senate Committee on Banking, Housing, and Urban Affairs Subcommittee on National Security and International Trade and Finance Subcommittee on Economic Policy” (PDF). fincen.gov. Financial Crimes Enforcement Network. (19 November 2013).

Ciresan, Dan; Meier, U.; Schmidhuber, J. (June 2012). "Multi-column Deep Neural Networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition: 3642–3649

Crosby, M., Pattanayak, P., Verma, S. and Kalyanaraman, V., (2016). Blockchain technology: Beyond Bitcoin. Applied Innovation, 2(6-10), p.71.

Dourado, Eli dan Jerry Brito. (2014). Cryptocurrency.The New Palgrave.

Elman, Jeffrey L. (1990). "Finding Structure in Time". Cognitive Science. 14 (2): 179–211. doi:10.1016/0364-0213(90)90002-E.

Han, J., Pei, J. and Kamber, M., (2011). Data Mining : concepts and techniques. Elsevier.

Herdianto. 2013. Prediksi Kerusakan Motor Induksi Menggunakan Metode Jaringan Saraf Tiruan Backpropagation. Tesis. Universitas Sumatera Utara : Medan

Hileman, G. and Rauchs, M., (2017). Global Cryptocurrency benchmarking study. Cambridge Centre for Alternative Finance, 33.

Iansiti, Marco; Lakhani, Karim R. (January 2017). "The Truth About Blockchain". Harvard Business Review. Harvard University

Jacobs, E., (2011). Bitcoin: a bit too far?. Journal of Internet Banking and Commerce, 16(2), p.1.

Kuhlman, Dave(2013). "A Python Book: Beginning Python, Advanced Python, and Python Exercises". Section 1.1..

Kurihara, Yutaka, and Akio Fukushima. "The market efficiency of Bitcoin: A weekly anomaly perspective." Journal of Applied Finance and Banking 7.3 (2017): 57.

Larose D, T., (2005). Discovering knowledge in data : an introduction to Data Mining, Jhon Wiley & Sons Inc.

Larose, Daniel T., and Larose, Chantal D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining Second Edition. New Jersey: John Wiley & Sons Inc.

Lustig, Caitlin, and Bonnie Nardi. "Algorithmic authority: The case of Bitcoin." 2015 48th Hawaii International Conference on System Sciences. IEEE, (2015).

Wes McKinney (2011). "Pandas: a Foundational Python Library for Data Analysis and Statistics". Dikutip tanggal 18 November 2019 pada ebook scribd.com

Narayanan, A., Bonneau, J., Felten, E., Miller, A. and Goldfeder, S., (2016). Bitcoin and Cryptocurrency technologies: A comprehensive introduction. Princeton University Press.

Ni, Xianjun. "Research of Data Mining based on Neural Networks." World Academy of Science, Engineering and Technology 39 (2008): 381-384.

Nian, L.P. and Chuen, D.L.K., (2015). Introduction to Bitcoin. In Handbook of Digital Currency (pp. 5-30). Academic Press.

Olah, Christoper (27 Agustus 2015), “Understanding LSTM Network”. Dikutip tanggal 5 Desember 2019, dari http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Olson, D.L. and Delen, D., (2008). Advanced Data Mining techniques. Springer Science & Business Media.

Pilkington, M., (2016). 11 Blockchain technology: principles and applications. Research handbook on digital transformations, 225.

Primartha, Rifkie. (2018). “Belajar Machine Learning Teori dan Praktik. Bandung : Informatika Bandung.

Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117

Schueffel, Patrick (2017). The Concise Fintech Compendium. Fribourg: School of Management Fribourg/Switzerland. Archived from the original on 24 October 2017.

SOVBETOV, Yhlas. (2018). Factors Influencing Cryptocurrency Prices:

Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero. Journal of

Economics and Financial Analysis, Vol:2, No:2 (2018) 1-27

Siegel, Eric (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1st ed.). Wiley. ISBN 978-1-1183-5685-2.

Yelowitz, A. and Wilson, M., (2015). Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters, 22(13), pp.1030-1036.




DOI: http://dx.doi.org/10.24014/rmsi.v8i2.17233

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