<![CDATA[The degradation of air quality in metropolitan areas, such as Jakarta, constitutes a significant environmental and public health challenge, contributing directly to an elevated risk of various diseases. The primary objective of this study is to develop and evaluate the effectiveness of an air quality classification model based on a Convolutional Neural Network (CNN), with a specific focus on addressing class imbalance using the hybrid resampling technique SMOTE+ENN. Utilizing a historical dataset from the HI Jakarta Station spanning 2010-2021, the model leverages key pollutant parameters (PM10, SO₂, CO, O₃, and NO₂) to classify air quality according to the Indonesian Air Quality Index (ISPU) standard. To mitigate the inherent challenge of class imbalance within the dataset, this study conducts a comparative analysis between a baseline CNN model and an optimized model enhanced with the hybrid resampling technique, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbours (SMOTE + ENN). The dataset was partitioned into an 80% training set and a 20% testing set. Empirical results demonstrate that the application of SMOTE + ENN yields a substantial improvement in performance. The final optimized model achieves a superior accuracy of 98.98%, significantly outperforming the baseline model. This outcome confirms that integrating CNN with the SMOTE + ENN strategy produces a highly effective and robust framework for air quality classification in Jakarta. Nonetheless, subsequent validation on more diverse datasets is recommended to ascertain the model's generalization capabilities and long-term reliability.]]>
