Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System
Abstract: Rainfall forcasting
is a non-linear forecasting process that varies according to area and strongly
influenced by climate change. It is a difficult process due to complexity of
rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy
Inference System (ANFIS) with hybrid learning method give high prediction for
rainfall as a forecasting model. Thus, in this study we investigate the
efficient membership function of ANFIS for predicting rainfall in Banyuwangi,
Indonesia. The number of different membership functions that use hybrid
learning method is compared. The validation process shows that 3 or 4
membership function gives minimum RMSE results that use temperature, wind speed
and relative humidity as parameters.
Author: Gusti Ahmad Fanshuri
Alfarisy, Wayan Firdaus Mahmudy
Journal Code: jptinformatikagg160010
