An Implementation of Fuzzy Inference System for Onset Prediction Based on Southern Oscillation Index for Increasing the Resilience of Rice Production Against Climate Variability

Abstract

Riee production system in lndonesia is very sensitive to global phenomena, especially the El Nino phenomenon. Information regarding the onset of rainy season is important to increase the resilience of the production system. This paper is focused on the implementation of Fuzzy lnfcrence System (FIS) as a technique for predieting the onset of rainy season based on the Southern Oscillation Index (SOl) data in the months of' July, August, September and Octobcr. There are two data seh uscd: the 501 data from the year 1877 to 2011 and the rainy scason onset in the District of Indramayu. Fuzzy set memberships and the set of rulcs are deslgned by lnvesttgatlng the two sets of data (via visualization and c1ustering). The prediction system is vcrlfled by using the actual data from the district. The result of the verjfication shows that the corrclation bctwecn the rainy onsct and its predictcd value is 0.68. Even though the prediction accuracy is relativcly cornpctltive comparcd to the existing methods, the requirement of the use of the SOl variablcs from the month of October renders the model less useful in practice, since it would be mostly too late to wait until October to pcrforrn the predietion. Further research can be developcd which lntcgratcs Markov Chain method to ovcrcome this problem.