Miko Septa Pratama, Heni Sulistiani

Machine Learning Comparative Analysis of SVR Method with RBF Kernel and Random Forest for Bitcoin Price Prediction

Introduction

Machine learning comparative analysis of svr method with rbf kernel and random forest for bitcoin price prediction. Compare SVR (RBF Kernel) and Random Forest Machine Learning methods for Bitcoin price prediction on volatile data (2018-2025). Random Forest yields more accurate results.

Abstract

This study aims to determine how accurate machine learning predictions are for predicting Bitcoin prices using the SVR With RBF Kernel and Random Forest methods. This study was conducted because Bitcoin’s volatility is so high that it is difficult to predict. Therefore, this study uses two different methods to allow for a more objective evaluation of model characteristics on volatile data. The dataset was obtained through Kaggle  with a Bitcoin price dataset from 2018 to October 2025, totaling 2,856 datasets in CSV format. After training both methods on the same dataset, price prediction results were obtained. Support Vector Regression (SVR) With RBF Kernel achieved a relatively  high data evaluation result with an MAE of 10866.882878735294, MSE of 204836847.5591309, and RMSE of 14312.12239883138, while the Random Forest method achieved a low data evaluation result with an MAE of 19342.47, MSE of 659671833.13, and RMSE of 25684.08.  The result of these two methods show a significant difference, with Random Forest more closely aligning with the acual data, with a lower evaluation value and producing values closer to the actual data. This research was conducted to determine the accuracy of the Support Vector Regression (SVR) with RBF Kernel and Random Forest algorithms. It is concluded that both methods make good predictions, only the Random Forest method is closer to the actual Bitcoin price.

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