Prediksi Awal Musim Hujan Menggunakan Adaptive Neuro- Fuzzy Inference System Pada Studi KasusKabupaten Indramayu

Abstract

Climate anomalies cause the onset of the rainy season changes in Indonesia. In agriculture, the change complicates to determine the precise period of cultivation. The fault on the determination increases crop failure by droughtness. As the one of the national granary, Indramayu depends on the happening climate in the rice producing absolutely. This research aims to predict the onset of rainy season in Indramayu using Adaptive Neuro-Fuzzy Inference System (ANFIS). The data used in this study is Southern Oscillation Index (SOl) from Mei to August as predictor and the onset of the rainy season from 1971 to 2010. 501 perfomed on the interval data reduction, so there are 7 intervals. There are five rain regions in Indaramayu and the average rain region to be predicted using their own ANFIS model. The prediction result was evaluated using the root mean squared error and the squared correlation coefficient. The best prediction result was obtained at interval [-7,7] with the average of the root mean squared error was 1.95 dasarian and the squared coefficient correlation was 0.33.