Analisis Pemodelan Regresi Pembelajaran Mesin untuk Estimasi Konsentrasi PM 2.5 di Jakarta: Pendekatan dan Implikasinya terhadap Kualitas Udara - Brilliant Muhammad Al Hadid Deva Sudarjo (Yayasan Kita Menulis - JoCPES, 2022)
Air pollution by fine particulate matter (PM2.5) significantly impacts public health and environmental stability. As an air pollutant, PM2.5 is influenced by climate factors such as temperature, humidity, and wind patterns, all of which fluctuate due to climate change. This literature review explores the application of machine learning (ML) in predicting and analyzing PM2.5 behavior, focusing on three primary methods: Support Vector Regression (SVR), Random Forest (RF), and Neural Networks (NN). Based on 20 studies, this review compares the strengths and limitations of each method, evaluating how ML techniques address the complexity and variability of climate data in the context of PM2.5.