Comparative Analysis of Cryptocurrency Prediction based on Deep Learning, Decision Tree, Gradient Boosted Tree, Random Tree, and k-NN Model
DOI:
https://doi.org/10.59395/ijadis.v5i2.1338Keywords:
Bitcoin Machine Learning Prediction Cryptocurrency, Bitcoin, Machine Learning, Prediction, CryptocurrencyAbstract
Cryptocurrency being a digital or virtual currency that uses cryptography to secure transactions and control the creation of new units. Bitcoin, one of the most popular cryptocurrency, offers various advantages such as security, transparency, and efficiency. The value of Bitcoin can change over time, similar to the regular currencies, and the need to predict the value can be as important as those in the regular. The prediction can be done by multiple algorithms. The purpose of this research is to compare five algorithms in predicting bitcoin value based on Root Mean Squared Error (RMSE) and Squared Error (R2). The five algorithms compared can model the prediction of changes in the bitcoin cryptocurrency, effectively. Based on the experiment, Random Forest outperformed the other algorithms based on its RMSE and R2 result
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