Prediksi Intensitas Curah Hujan Menggunakan Metode Jaringan Saraf Tiruan Backpropagation - Defanto Hanif Yoranda; Muhammad Tanzil Furqon; Mahendra Data (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
The intensity of rainfall is quite difficult to predict. Many things can be the factor of rainfall, such as temperature, wind speed, humidity, air pressure, and others. This rainfall factor is a major component that is difficult to predict and calculated, therefore rainfall forecasting is a very interesting thing to discuss, because it will be very useful for various things. Many forecasting methods can be used for forecasting, such as the Backpropagation Neural Network used in this study. This research will use time-series data, monthly rainfall data obtained from Kab. Ponorogo. The best result of this research is test MAPE of 20.28% obtained from training using data from Balong rain gauge station. The training process uses 10 neurons on the input layer, training data from 1997 to 2015, test data in 2016, 40 neurons on the hidden layer, a MAPE limit of 20%, and a maximum of 200000 iterations. Test MAPE is classified as not very well and too high due to there are many 0 values in the data.