XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION

  • Nur Alamsyah (1*) Universitas Informatika Dan Bisnis Indonesia
  • Budiman Budiman (2) Universitas Informatika Dan Bisnis Indonesia
  • Titan Parama Yoga (3) Universitas Informatika Dan Bisnis Indonesia
  • R Yadi Rakhman Alamsyah (4) Universitas Informatika Dan Bisnis Indonesia

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v20i2.5569

Keywords: forest fire prediction, hyperparameter optimization, XGBoost

Abstract

Climate change and increasing global temperatures have increased the frequency and intensity of forest fires, making fire risk evaluation increasingly important. This study aims to improve the accuracy of predicting forest fuel drought conditions (Drought Code) by using the XGBoost algorithm optimized with RandomizedSearchCV. The research methods include collecting data related to forest fires, preprocessing data to ensure quality and consistency, and using RandomizedSearchCV for XGBoost hyperparameter optimization. The results showed that the optimized XGBoost model resulted in a decrease in Mean Squared Error (MSE) and an increase in R-squared value compared to the default model. The optimized model achieved an MSE of 0.0210 and R2 of 0.9820 on the test data, indicating significantly improved prediction accuracy for forest fuel drought conditions. These findings emphasize the importance of hyperparameter optimization in improving the accuracy of predictive models for forest fire risk assessment.

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Published
2024-09-23
How to Cite
Alamsyah, N., Budiman, B., Yoga, T., & Alamsyah, R. Y. (2024). XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION. Jurnal Pilar Nusa Mandiri, 20(2), 103-110. https://doi.org/10.33480/pilar.v20i2.5569
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