Prediksi Kualitas Udara Menggunakan Metode CatBoost
DOI:
https://doi.org/10.14421/jiska.2025.10.2.249-258Keywords:
Prediction, Air Quality, Gradient Boosting, CatBoost, GridSearchCVAbstract
Air is essential for life, but industrial activities, forest fires, cigarette smoke, and transportation contribute to air pollution. AirVisual AQI 2024 data ranks Jakarta in 11th place globally, with the highest level of pollution, reaching 127, which is unhealthy for sensitive groups and poses a risk of causing serious illnesses, including skin and respiratory diseases. This research uses the CatBoost method to predict the air quality index using Jakarta SPKU data taken from Kaggle. The data is processed through pre-processing and divided into four models with different comparisons of training and testing data. Each model was tested with the parameters iteration, depth, learning rate, and l2_leaf_reg, using GridSearchCV to find the optimal combination. The results show that the model with 90% training data and 10% testing data provides the best accuracy of 97%, due to the larger proportion of training data. This research demonstrates that the CatBoost method can yield accurate air quality predictions, which is crucial in supporting efforts to mitigate the impact of pollution and enhance public health.
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