Prediksi Kualitas Udara Menggunakan Metode CatBoost

Air is important for life, but industrial activities, forest burning, cigarette smoke and transportation increase air pollution. AirVisual AQI 2024 data places Jakarta in 11th place in the world with the highest level of pollution, reaching 127 which is unhealthy for sensitive groups, and poses a risk of causing serious illnesses such as 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 best 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 shows that the CatBoost method can provide accurate air quality predictions, which is important to support efforts to reduce the impact of pollution and improve public health.