Peramalan KLBCampakMenggunakanGabunganMetode JST Backpropagationdan CART

https://doi.org/10.22146/ijccs.3495

Sulistyowati Sulistyowati(1*), Edi Winarko(2)

(1) 
(2) 
(*) Corresponding Author

Abstract


Forecasting Measles Outbreak  in an area is necessary because to prevent widespread occurrence in an area. One way that is done in this study is to predict the incidence of measles by using a combination of backpropagation ANN and CART. Backpropagation ANN is used to predict the incidence of measles periodic data, then the CART method used to perform the determination of an outbreak or non-outbreak area.

Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules.

Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Determination of the number of windows of a backpropagation neural network forecasting on each attribute gives different results and directly affects the forecasting results. ANN can do the forecasting in time series using siliding window with accuracy 90.01% and then CART method can be use for classification with accuracy 83.33%.


Keywords


KLB, Measles, Forecasting, Backprpagation, CART

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References

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DOI: https://doi.org/10.22146/ijccs.3495

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