Implementation of Random Forest Algorithm for Air Quality Classification: A Case Study of DKI Jakarta's Air Quality Index

Air quality monitoring and classification in urban environments present significant challenges for environmental management and public health policy. This study implements an optimized Random Forest (RF) algorithm to classify air quality levels in DKI Jakarta, Indonesia, using the Air Quality Index (AQI) data from 2021. The analysis incorporates six key pollutants: PM10, PM2.5, NO2, SO2, CO, and O3, with data collected from the Environmental Management Agency of DKI Jakarta. The RF model was developed using 5000 decision trees with optimized parameters (mtry=2) and evaluated through stratified sampling with a 70:30 train-test split. The model achieved an exceptional accuracy of 99.09% with a low Out-of-Bag (OOB) error rate of 2.35%. Feature importance analysis revealed that particulate matter (PM2.5 and PM10) were the most influential factors, collectively accounting for 78.70% of the model's decision-making process. The high performance metrics across all air quality categories (Good, Moderate, and Unhealthy) demonstrate the model's reliability in classification tasks. This research provides insights into environmental monitoring and policymaking, presenting a framework adaptable to other urban settings. The findings highlight the crucial role of particulate matter in air quality assessment and suggest targeted strategies for pollution control.