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Abstract. Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies.

The uncertain pattern of rainfall causes apple farmers to be difficult in determining the flowering time that resulted in the apple harvest becomes not maximal. Many methods are used to predict rainfall, one of which is Fuzzy Inference System (FIS) Tsukamoto. Earlier research using this method managed to get a fairly small Root Mean Square Error (RMSE) value. In this research, FIS Tsukamoto method is used to create rainfall prediction modeling in four locations in Batu, East Java with the aim to get a small RMSE also. Tsukamoto's FIS method is used to predict rainfall with time series data from 2005 to 2014. The result of this research is the prototype of Tsukamoto FIS method that can be used to predict rainfall with RMSE error value in Junggo area of 9,196, in Pujon area of 9,407 , in Tinjomulyo area of 8,798, in Ngujung area of 8,825.

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