Prediksi Potensi Suatu Wilayah Menggunakan Machine Learning - Tarwoto; Ratri Ismayanti; Vita Dwi Utami; Vellyn Chalista Elfanza (Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET), 2025)
Electricity has become a basic need for some human beings because all activities are almost related to electricity. Indonesia has several power plant projects and the largest power plant is generated from PLTU which can have an impact that we feel is greenhouse gas emissions and bad air pollution and also relies heavily on coal while the natural resources are not renewable with this fact if we reduce the use of coal it will be a boomerang for Indonesia itself. This research aims to predict areas that can potentially become Solar Power Plants with a machine learning regression model approach. So hopefully this research can be a reference in the development of Solar Power Plants in Indonesia. The methods used are Linear Regression (LR), Lasso Regression (LR), Ridge Regression (RR), and Support Vector Regression (SVR). The R2 coefficients for solar radiation were 0.924; 0.910; 0.917; 0.949; and 0.987, respectively.