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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.

Article Details

Author Biographies

Gusti Ahmad Fanshuri Alfarisy, Universitas Brawijaya

Intelligence System Research Group

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science
How to Cite
Alfarisy, G. A. F., & Mahmudy, W. F. (2017). Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System. Journal of Information Technology and Computer Science, 1(2), 65–71.


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