KLASIFIKASI INDEKS STANDAR PENCEMARAN UDARA DI JAKARTA DENGAN METODE K-NEAREST NEIGHBORS DAN ARTIFICIAL NEURAL NETWORK
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Abstract
This research looks at how two machine learning algorithms, K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), function to predict the Air Pollution Standard Index (ISPU) in Jakarta. The data used is from 2014 to 2019 and includes ten features, including target classes and air quality parameters that are based on ISPU. After the data cleaning and sharing stage, both algorithms were trained and tested based on accuracy, precision, recall, and F-1 score metrics. The results show that KNN has 98% accuracy, while ANN has 97% accuracy. Thus, KNN is more suitable for predicting the Jakarta ISPU dataset. This research provides insight into how on those algorithms can be used to address urban air quality issues.
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