PREDIKSI KASUS COVID-19 DI INDONESIA MENGGUNAKAN METODE BACKPROPAGATION DAN FUZZY TSUKAMOTO - Fra Siskus Dian Arianto; Noviyanti P (Universitas Asahan, 2020)
Abstract - Pandemic COVID-19 has so far not subsided. This outbreak has spread to almost all countries in the world. As a result of this pandemic community activity and economy has decreased. The COVID-19 pandemic itself appeared in Indonesia precisely on March 2, 2020. 2 people tested positive for being infected with COVID-19 so that it was referred to as case 1 and case 2. After being detected a pandemic COVID-19 in Indonesia, Indonesia experienced an increase in cases every day positive COVID-19. The purpose of this research is to be able to obtain models in predicting the addition of COVID-19 cases in Indonesia based on time series data. In this research, the development of the Fuzzy Tsukamoto method is carried out to produce learning rate momentum which is then used in building network architecture in the Backpropagation method and produces a prediction model for adding COVID-19 cases in Indonesia. The model produced by conducting a network architecture experiment is the R-value (correlation coefficient) of 0.84278 and the prediction simulation produces an MSE of 1.632337 on the normalization data 16y=-0,7474+1,880411+e-0,5004+(1,6779)(xt)