Pemanfaatan Pemodelan Machine Learning dalam Memprediksi Parameter Kualitas Udara Nitrogen Dioksida (NO2) Berdasarkan Algoritma Extra Trees Regression di DKI Jakarta

Muhammad Aulia Zikri (Program Studi Meteorologi, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia)
I Wayan Jyesta Jaya Taruna (Program Studi Meteorologi, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia)
Juang Merdeka (Program Studi Meteorologi, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia)
Agung Hari Saputra (Program Studi Meteorologi, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia)

JC-T (Journal Cis-Trans) : Jurnal Kimia dan Terapannya
Vol. 7 No. 2 (2023)

The study examined the air quality in DKI Jakarta, mainly on the nitrogen dioxide (NO2) parameters, using the Extra Trees Regression model to predict the NO2 index. The study used the 2022 NO2 time series data, which showed no significant long-term trends and indicated stationary and random data. Periodogram, histogram, and Q-Q plot analysis showed normal distribution with minor deviations. No significant autocorrelation was found between actual NO2 data and model data, indicating the possibility of white noise. Evaluation of models with parameters such as MASE, MAE, RMSE, MAPE, SMAPE, and R2 showed good model performance. An R2 value of 73.14% indicates a model's ability to explain actual data variability. Although the Extra Trees Regression model follows seasonal patterns, there is an inconsistency between actual values and predictions at some points. This indicates a potential overfitting or difficulty in capturing specific data patterns. This research provides modeling information that is suitable for predicting air quality in DKI Jakarta.