Komparasi Kinerja Algoritma Machine Learning dalam Memprediksi Posisi Finish Pembalap Formula 1
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1190Keywords:
Formula 1; Machine Learning; Ridge Regression; Support Vector Regression; Random Forest Regression; Finish Position PredictionAbstract
Formula 1 is a motorsport competition with high complexity in determining drivers’ finish positions. Research on Formula 1 finish position prediction using regression approaches remains limited compared to classification, leaving its performance underexplored. This study aims to compare the performance of Ridge Regression, Support Vector Regression (SVR), and Random Forest Regression in predicting Formula 1 drivers’ finish positions, identify key factors, and predict finish positions at the 2026 Miami Grand Prix. The dataset, sourced from Kaggle, comprises historical Formula 1 data from 2021-2025 and data from the first three 2026 season races. This study applies preprocessing, feature engineering, feature selection, hyperparameter tuning, model evaluation, and interpretation of the best model using Permutation Feature Importance and SHAP. Results show that SVR performed best, with MAE 2.8685, RMSE 4.0919, R2 0.4946, MAPE 37.2987%, and Pearson Correlation Coefficient 0.7233. SVR’s MAE and MAPE outperformed Ridge Regression (MAE 3.1000; MAPE 56.8081%) and Random Forest Regression (MAE 3.1611; MAPE 57.6259%), although this MAPE remains high. Position Qualifying was the most influential feature, followed by Driver Rank Previous, Constructor Rank Previous, and Grid. In the 2026 Miami Grand Prix forecasting, SVR successfully predicted the race winner, although prediction gaps remained considerable for several drivers. This study contributes a comparative framework of linear and non-linear regression models for predicting Formula 1 finish positions, an aspect not widely explored, along with prediction interpretation using PFI and SHAP.
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