PM2.5 Index Prediction in Central Jakarta Using Pollution and Meteorological Data With Cnn-Lstm

¹ Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia, 11480
² Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia, 11480

^* Corresponding author: stevanus.febrian@binus.ac.id, syarifah.permai@binus.ac.id

Abstract

Jakarta is among the most polluted cities globally, with PM_2.5 posing the greatest health risks. Accurate prediction of PM_2.5 levels is therefore essential to support early warning systems and public health decisions. This study develops a hybrid deep learning model, CNN-LSTM, to forecast daily PM_2.5 concentrations in Central Jakarta for the next seven days. The dataset combines air pollutant records from Satu Data Jakarta and meteorological data from BMKG, covering January 2023 to December 2024. Data preparation included cleaning, handling missing values, outlier analysis, feature selection using Pearson correlation and linear regression, and normalization. Model performance was evaluated using MSE, MAE, RMSE, and MAPE, and compared against standalone CNN and LSTM models under two data configurations: pollutant-only and pollutant & meteorology. Results show that CNN-LSTM achieved the lowest MSE and RMSE when using combined data, indicating high predictive accuracy. However, the LSTM model with pollutant-only input provided the most consistent and efficient results, with lower error values across all metrics. These findings suggest that historical pollutant patterns alone are sufficient for short-term PM_2.5 forecasting, while meteorological factors offer limited additional benefit. The study demonstrates the potential of deep learning in supporting air quality monitoring and public health protection.