APPLICATION OF MACHINE LEARNING FOR BITCOIN EXCHANGE RATE PREDICTION AGAINST US DOLLAR

  • Ninuk Wiliani (1*) Institut Teknologi dan Bisnis BRI
  • Rizki Hesananda (2) Institut Teknologi dan Bisnis Bank Rakyat Indonesia
  • Nidya Sari Rahmawati (3)
  • Erdham Hestiadhi Prianggara (4) Institut Teknologi dan Bisnis Bank Rakyat Indonesia

  • (*) Corresponding Author
Keywords: Prediction, Training, Bitcoin, Testing

Abstract

Predicting a currency Exchange rate and performing analysis is an action to try to determine the price valuation of a currency or other financial instrument traded on an exchange platform. Bitcoin is a consensus network that enables new payment systems and fully digital money. Bitcoin is the first decentralized peer to peer payment network that is fully controlled by its users without any central authority or intermediary. From the user's point of view, Bitcoin is like cash in the internet world. Bitcoin can also be viewed as the most prominent triple bookkeeping system in existence today. The change in Bitcoin's behavior against the US dollar is influenced by many factors. Basic or economic factors that may be affected include inflation rates and money supply. In this study, data was collected by obtaining all data through the API provided by binance.com and labeled with the specified attribute. The modeling is done by using the rapidminer application. The process begins by taking training data that has been provided previously. The next stage is the data testing process, all operators that have been previously determined are connected and tested using the Linear Regression operator. The purpose of testing this data is to predict stock prices from the testing data that has been made by the Split Data operator, which is 19% of the total data that has been prepared.

Downloads

Download data is not yet available.

References

R. Auer and R. Böhme, “Central bank digital currency: the quest for minimally invasive technology,” BIS Work. Pap., no. 948, 2021, [Online]. Available: www.bis.org.

F. Diaz et al., “Digital Menu Pada X Cafe Berbasis Desktop Graphical User Interface Dengan Visual Basic 2010 dan Microsoft Access,” J. Rekayasa Inf., vol. 6, no. 1, p. 43, 2017, doi: 10.1017/CBO9781107415324.004.

Halimatusyakdiah, A. Kosim, and E. Meirawati, “Analisis Financial Distress pada Industri Kosmetik yang Terdaftar di Bursa Efek Indonesia (BEI) untuk Memprediksi Potensi Kebangkrutan Perusahaan,” Akuntabilitas J. Penelit. dan Pengemb. Akunt., vol. Vol 9 (2), no. 2, pp. 125–140, 2015.

Y. Achsanty, H. Abrianto, and N. Wiliani, “Implementasi Metode Algoritma KVC Untuk Pengamanan Pesan,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 3, no. 3, p. 73, 2018, doi: 10.30645/jurasik.v3i0.62.

U. Ravindran, “A Review Paper on Regulating Bitcoin Currencies,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 6, no. 4, pp. 4136–4140, 2018, doi: 10.22214/ijraset.2018.4682.

G. Differences, “Real Time Defect Detection Method for rinted Images Based On Grayscale and Gradient Differences,” vol. 11, no. 1, pp. 180–188, 2018, doi: 10.25103/jestr.111.22.

L. Zhuang, Z. Zhang, and L. Wang, “The automatic segmentation of residential solar panels based on satellite images: A cross learning driven U-Net method,” Appl. Soft Comput. J., vol. 92, p. 106283, 2020, doi: 10.1016/j.asoc.2020.106283.

I. P. Dhani and A. . G. S. Utama, “Pengaruh Pertumbuhan Perusahaan, Struktur Modal, Dan Profitabilitas Terhadap Nilai Perusahaan,” J. Ris. Akunt. Dan Bisnis Airlangga, vol. 2, no. 1, pp. 135–148, 2017, doi: 10.31093/jraba.v2i1.28.

K. Agroui, M. Pellegrino, and F. Giovanni, “Analysis Techniques for Photovoltaic Modules Based on Amorphous Solar Cells,” Arab. J. Sci. Eng., vol. 42, no. 1, pp. 375–381, 2017, doi: 10.1007/s13369-016-2050-5.

H. Y. Kim and J. S. Cho, “Data governance framework for big data implementation with NPS Case Analysis in Korea,” J. Bus. Retail Manag. Res., vol. 12, no. 3, pp. 36–46, 2018, doi: 10.24052/jbrmr/v12is03/art-04.

A. Sinha, O. S. Sastry, and R. Gupta, “Detection and characterisation of delamination in PV modules by active infrared thermography,” Nondestruct. Test. Eval., vol. 31, no. 1, pp. 1–16, 2016, doi: 10.1080/10589759.2015.1034717.

T. K. Wen and C. C. Yin, “Crack detection in photovoltaic cells by interferometric analysis of electronic speckle patterns,” Sol. Energy Mater. Sol. Cells, vol. 98, pp. 216–223, 2012, doi: 10.1016/j.solmat.2011.10.034.

S. Deitsch et al., “Automatic classification of defective photovoltaic module cells in electroluminescence images,” Sol. Energy, vol. 185, pp. 455–468, Jun. 2019, doi: 10.1016/j.solener.2019.02.067.

L. A. Sunjoyo, R. G. Santosa, and K. A. Nugraha, “Implementasi transformasi Haar Wavelet Untuk Deteksi Citra Jeruk Nipis Yang Busuk,” Informatika, vol. 12, no. 2, pp. 165–173, 2016.

Published
2022-02-18
How to Cite
[1]
N. Wiliani, R. Hesananda, N. Rahmawati, and E. Prianggara, “APPLICATION OF MACHINE LEARNING FOR BITCOIN EXCHANGE RATE PREDICTION AGAINST US DOLLAR”, jitk, vol. 7, no. 2, pp. 67-74, Feb. 2022.
Article Metrics

Abstract viewed = 87 times
PDF downloaded = 57 times

Most read articles by the same author(s)