Rainfall prediction in Tengger region Indonesia using Tsukamoto fuzzy inference system

Wahyuni, Ida; Mahmudy, Wayan Firdaus; Iriany, Atiek

doi:10.1109/icitisee.2016.7803061

Cited by 38 publications

(40 citation statements)

References 5 publications

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“…The process of calculating the Tsukamoto fuzzy inference system was described in Wahyuni, Mahmudy, and Iriany [5]. The fitness calculation on chromosome 1 is shown in Table 2.…”

Section: Calculation Fitness Functionmentioning

confidence: 99%

“…Application of the Generalized Space Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR) method resulted in RMSE for the Tutur area of 10.89. Research using similar data was conducted by Wahyuni, Mahmudy, and Iriany [5] for the same location but with a different method. The algorithm was used Tsukamoto Fuzzy Inference System (FIS) resulting in an RMSE for the Tutur area of 8.64.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the results of this research, Tsukamoto FIS was found to be the better prediction algorithm. In the study conducted by Wahyuni, Mahmudy, and Iriany [5], the limits of the fuzzy membership functions were still determined manually so that the obtained results did not improve, despite showing a better RMSE than previous studies. The fuzzy membership functions from this research should be optimized to obtain better prediction results.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters that were used as input parameters in Tsukamoto FIS were: rainfall data from 10 days prior (Zt-1), rainfall data from 20 days prior (Zt-2), rainfall data from 170 days prior (Zt-17), and rainfall data from 340 days prior (Zt-34). The rainfall data and input parameters used were similar to those used in the previous study conducted by Wahyuni, Mahmudy, and Iriany [5]. This was done to see whether or not the results of rainfall prediction with membership function optimization using a genetic algorithm produced better predictions.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method

Wahyuni

Mahmudy

2017

J. ICT Res. Appl.

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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.

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“…The process of calculating the Tsukamoto fuzzy inference system was described in Wahyuni, Mahmudy, and Iriany [5]. The fitness calculation on chromosome 1 is shown in Table 2.…”

Section: Calculation Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method

Wahyuni

Mahmudy

2017

J. ICT Res. Appl.

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“…There are several studies that have been done previously that serve as a reference in this study. Wahyuni(2016), researchers mentions, in the agricultural sector is highly dependent on climate, in this case is rainfall. Therefore, rainfall prediction is needed for farmers' agricultural commodities are not disturbed because of climate.…”

Section: Literature Reviewmentioning

confidence: 99%

Honey Yield Prediction Using Tsukamoto Fuzzy Inference System

Hastono¹,

Santoso²,

Pranowo³

2017

EECSI

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Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides a solution for prediction of honey yield type Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield using FIS Tsukamoto closed the real honey yield with RMSE value of 9.44933860119277.

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