Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction

Deny Haryadi - Information Technology, Institut Teknologi Telkom Jakarta, Jl. Daan Mogot KM 11, Cengkareng, Jakarta Barat, 11710, Indonesia
Arif Rahman Hakim - Digital Business, Universitas Medika Suherman, Jl. Industri Pasir Gombong, Cikarang, Bekasi, 17530, Indonesia
Dewi Marini Umi Atmaja - Digital Business, Universitas Medika Suherman, Jl. Industri Pasir Gombong, Cikarang, Bekasi, 17530, Indonesia
Syifa Nurgaida Yutia - Information Technology, Institut Teknologi Telkom Jakarta, Jl. Daan Mogot KM 11, Cengkareng, Jakarta Barat, 11710, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.1-2.945

Abstract


Cryptocurrency investment is an investment instrument that has high risk but also has a greater advantage than other investment instruments. To make a big profit, investors need to analyze cryptocurrency investments to predict the price of the cryptocurrency to be purchased. The highly volatile movement of cryptocurrency prices makes it difficult for investors to predict those prices. Data mining is the process of extracting large amounts of information from data by collecting, using data, the history of data relationship patterns, and relationships in large data sets. Support Vector Regression has the advantage of doing accurate cryptocurrency price predictions and can overcome the problem of overfitting by itself. Polkadot is one of the cryptocurrencies that are often used as investment instruments in the world of cryptocurrencies. Polkadot cryptocurrency price prediction analysis using the Support Vector Regression algorithm has a good predictive accuracy value, including for Polkadot daily closing price data, namely with a radial basis function (RBF) kernel with cost parameters C = 1000 and gamma = 0.001 obtained model accuracy of 90.00% and MAPE of 5.28 while for linear kernels with parameters C = 10 obtained an accuracy of 87.68% with a MAPE value of 6.10. It can be concluded that through parameter tuning, the model formed has an accuracy value and the best MAPE is to use a radial kernel basis function (RBF) with cost parameters C = 1000 and gamma = 0.001. The results show that the Support Vector Regression method is quite good if used for the prediction of Polkadot cryptocurrencies.

Keywords


Prediction; cryptocurrency; support vector regression; time series.

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References


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