<![CDATA[Accurate air pollution predictions are crucial for public health and environmental management, but achieving high prediction accuracy remains a challenge due to the complexity of temporal patterns in pollution data. This study aims to improve performance of Long Short-Term Memory (LSTM) by optimizing hyperparameters tuning based Chaotic Particle Swarm Optimization (CPSO) for air pollution predictions. Hyperparameter optimization included the number of hidden layers, neurons, activation functions, loss functions, optimizers, batch sizes, and epochs. The proposed model LSTM-CPSO compared to other models, baseline LSTM and PSO-LSTM, to predict the concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 in Jakarta based on Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The experimental results show that CPSO-LSTM achieves superior performance with MSE 0.012105, MAE 0.086356, RMSE 0.110022, and MAPE 32.31%, outperforming the baseline LSTM by 38.1% on the MSE metric and 11.9% on MAPE. Interestingly, LSTM-CPSO produces better architecture with 2 hidden layers and 91 neurons than LSTM-PSO that requires 7 hidden layers with 51 neurons. Similarly, LSTM-CPSO has shortest 5 training epochs better than LSTM-PSO with 16 epochs. This research demonstrates that chaos-based metaheuristic optimization can select the best hyperparameters to improve the performance of LSTM for air quality forecasting.]]>
