Prediksi Curah Hujan Wilayah Provinsi Yogyakarta dengan Algoritma Neural Network - rubangi rubangi; Arief Hermawan; Donny Avianto (JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer), 2021)
Emerging inaccurate information about the rainfall system can affect aspects of life. Inaccurate precipitation forecasting can be problematic, so it is necessary to predict precipitation with a high level of accuracy. Therefore, this study proposes a method with a neural network algorithm to predict rainfall to benefit the community. The data used in this study is daily precipitation from 2016 to 2021 from BMKG. Based on the test results, the data shows that the best neural network (NN) model is obtained from input layer 31, hidden layer 4, training cycle 1500, learning rate 0.01, and momentum 0.9, resulting in an error of 0.828. Based on the results with the smallest error, using the neural network method can be used to predict future precipitation with good accuracy.