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All Journal JOIV : International Journal on Informatics Visualization Tech-E JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Informatics and Communication Technology (JICT) IT For Society : Journal of Information Technology Jurnal Rekayasa elektrika Journal of Informatics and Communication Technology (JICT)
Deny Haryadi
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mathematics

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Journal : JOIV : International Journal on Informatics Visualization

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Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction Deny Haryadi; Arif Rahman Hakim; Dewi Marini Umi Atmaja; Syifa Nurgaida Yutia
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 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.
Co-Authors Adi Kuncoro Adi Kuncoro Amat Basri Amat Basri Andri Ariyanto Arif Rahman Hakim Arif Rahman Hakim Demi Adidrana Dewi Marini Umi Atmaja Fiqry Firdhan Pratama Putra Nabila Rahmania Az Zahra Ricka Maulida Rila Mandala Risma Adisty Nilasari Sasmi Hidayatul Yulianing Tyas Seandy Arandiant Rozano Sisilia Ratu Counsela Evrilia Syifa Nurgaida Yutia Umi Atmaja, Dewi Marini