THE PERFORMANCE ANALYSIS OF THE BEST MACHINE LEARNING MODEL FOR SULFUR DIOXIDE IN DKI JAKARTA

Abstract

A good clean air is one of crucial things for humans health. A place with good and clean air can prevent humans from various kinds of respiratory diseases. One of the factors that can influence the cleanliness of the air in an area is the composition of Sulfur Dioxide (SO2). This research focuses on analyzing sulfur dioxide (SO2) compositions in Jakarta over an eleven-year period. The objective is to identify the most effective model in predicting SO2 compositions, which is critical for public health and environmental management. The study incorporates quantitative methods, machine learning techniques, and statistical analysis. From this research there are three best models that has top performance, these are huber, exponential smoothing, and naïve forecaster. The result shows that naive model has the best performance with MASE of 0.3864, RMSSE of 0.3098, MAE of 2.8857, RMSE of 3.7735, MAPE of 0.0593, and SMAPE of 0.0623.