Implementation of the K-Nearest Neighbor Algorithm to Predict Air Pollution

Abstract

Air pollution is a serious issue that impacts air quality and human health. In this study, the K-Nearest Neighbor (KNN) algorithm is applied using Rapidminer software to predict air pollution levels. The research aims to predict air pollution levels based on various air quality parameters such as particulates, PM10, PM2.5, CO, NO2, SO2, and O3. By implementing the K-Nearest Neighbor algorithm in Rapidminer, the predicted values for air pollution data resulted in an accuracy of 93.94%. This study concludes that employing the K-Nearest Neighbor algorithm using Rapidminer software can be an effective method for predicting air pollution levels. With a strong accuracy rate of 93.94%, this can have a positive impact on both human health and the environment. The predictive model developed can aid decision-making and enhance awareness among the public regarding the importance of maintaining air quality management.