Penerapan Model Machine Learning (KNN, Random Forest, dan Naive Bayes) untuk Menganalisis Pengaruh Kualitas Udara terhadap COVID-19: Studi Normalisasi pada Data COVID-19

Irene Paskalita Ponamon(1*), Alz Danny Wowor(2),

(1) Universitas Kristen Satya Wacana, Salatiga, Indonesia
(2) Universitas Kristen Satya Wacana, Salatiga, Indonesia
(*) Corresponding Author

Abstract


The study was intended to analyze the effects of air quality on covid-19 in jagakconclusion, south Jakarta, using the machine learning classification algorithm: random forest, naive bayes, and k-herd prediction (KNN). The data used consisted of about 6,713 records that included case files of covid-19 and data of air quality such as pm10, so2, co, o3, and no2 obtained from the official air and source monitoring stations. The research process involves the stage of data preparation, normalization, exploration, and training and model evaluation. The results showed that random forest algorithm with 100 trees reached its highest level of accuracy, around 97.6% of the data had been modernized, and was consistently the best performance compared to naive bayes and KNN. Furthermore, analysis suggests that the normal of data significantly increases model performance. The conclusion from this study suggests that air quality affects the spread and severity of covid-19 in the region, and that the random forest model is the best option for prediction and analysis of the environmental impact on the covid-19 case. The results of this study are expected to be a reference to more effective development of health and environmental policies.

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DOI: https://doi.org/10.30645/kesatria.v6i2.596

DOI (PDF): https://doi.org/10.30645/kesatria.v6i2.596.g591

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