DOI: https://doi.org/10.14710/j.gauss.15.1.01-11

OPTIMASI XGBOOST DALAM PREDIKSI KECEPATAN KENDARAAN SECARA REAL-TIME : PERBANDINGAN METODE TUNING HYPERPARAMETER

*Panji Lokajaya Arifa  -  Program Studi Statistika dan Sains Data, Sekolah Sains Data, Matematika dan Informatika, Institut Pertanian Bogor, Jl. Raya Dramaga Kampus IPB, Dramaga, Bogor, Indonesia 16680, Indonesia
Kusman Sadik orcid scopus  -  Program Studi Statistika dan Sains Data, Sekolah Sains Data, Matematika dan Informatika, Institut Pertanian Bogor, Jl. Raya Dramaga Kampus IPB, Dramaga, Bogor, Indonesia 16680, Indonesia
Agus M Soleh orcid scopus  -  Program Studi Statistika dan Sains Data, Sekolah Sains Data, Matematika dan Informatika, Institut Pertanian Bogor, Jl. Raya Dramaga Kampus IPB, Dramaga, Bogor, Indonesia 16680, Indonesia
Cici Suhaeni orcid scopus  -  Program Studi Statistika dan Sains Data, Sekolah Sains Data, Matematika dan Informatika, Institut Pertanian Bogor, Jl. Raya Dramaga Kampus IPB, Dramaga, Bogor, Indonesia 16680, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Real-time vehicle speed prediction plays a vital role in the development of intelligent transportation systems aimed at improving traffic flow and safety. This study investigates the performance of the XGBoost algorithm enhanced with three hyperparameter tuning techniques: Grid Search, Bayesian Optimization, and Genetic Algorithm. A simulated dataset was constructed reflect diverse urban traffic scenarios, incorporating environmental variables such as weather, road conditions, and traffic density. The models were assessed using 5 and 10-fold cross-validation based on prediction metrics (MSE, RMSE, MAE and R²) as well as computational efficiency in terms of training and inference time. The findings reveal that Bayesian Optimization achieves the highest prediction accuracy, while Grid Search offers the fastest training time. Genetic Algorithm demonstrates a balanced trade-off between accuracy and computational efficiency, making it a competitive and practical choice. These results highlight the importance of selecting hyperparameter tuning strategies based on specific system needs in real-time traffic prediction using XGBoost. 

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Keywords: Xgboost; Hyperparameter Tuning; Grid Search; Bayesian Optimization; Genetic Algorithms

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