Analisis dan Perbandingan Algoritma SVR, XGBOOST, dan Lightgbm dalam Prediksi Cryptocurrency Ethereum - Ryan Anthony; Jechenthia Maria Taso; Stephen Yohanes Christopher; Ridhwan Ardiyansyah (Comit: Communication, Information and Technology Journal, 2026)
This study aims to analyze and compare the performance of three algorithms, namely Support Vector Regression (SVR) with a linear kernel, XGBoost, and LightGBM, in predicting the Price of Ethereum cryptocurrency based on daily historical data. The study uses Ethereum Price data in USD for the last five years obtained from the investing.com website. The variables used are Close, Open, High, and Low Prices. The study uses two data splitting scenarios: 80% training data and 20% testing data, and 70% training data and 30% testing data. This study also uses time step variations to test the effect of time dependency on algorithm performance. The results indicate that the LightGBM algorithm has the best performance compared to the other two algorithms with an average MAE value for High Price of 75.486, SVR has a value of 115.590, and XGBoost has a value of 77.314 in the 80% training data and 20% testing data split. In the 70% training data and 30% testing data split, the LightGBM algorithm still excels with an average MAE value for High Price of 78.228, SVR of 104.356, and XGBoost of 83.573. Other evaluations such as RMSE and R2 also show the superiority of the LightGBM algorithm. For the required computation time, the SVR algorithm outperforms the other two algorithms.