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)"> .Keywords -    Case COVID-19, Backpropagation Method, Tsukamoto Fuzzy Method, Prediction of COVID-19 cases. Abstrak – Pandemic COVID-19 sampai saat ini belum mereda. Wabah ini telah meluas dihampir seluruh negara didunia. Akibat dari pandemic ini aktivitas dan perekonomian masyarakat mengalami penurunan. Pandemic COVID-19 ini sendiri muncul di Indonesia tepatnya pada 2 Maret 2020. Terdapat 2 orang yang dinyatakan positif terinfeksi COVID-19 sehingga disebut sebagai kasus 1 dan kasus 2. Setelah terdeteksi adanya pandemic COVID-19 di Indonesia, setiap harinya Indonesia mengalami penambahan kasus positif COVID-19. Tujuan dilakukannya penelitian ini adalah untuk dapat memperoleh model dalam memprediksi penambahan kasus COVID-19 di Indonesia berdasarkan pada data time series. Pada penelitian ini dilakukan pengembangan terhadap metode Fuzzy Tsukamoto untuk menghasilkan learning rate momentum yang kemudian digunakan dalam membangun arsitektur jaringan pada metode Backpropagation dan menghasilkan sebuah model prediksi penambahan kasus COVID-19 di Indonesia. Model yang dihasilkan dengan melakukan 1 kali percobaan arsitektur jaringan adalah 16y=-0,7474+1,880411+e-0,5004+(1,6779)(xt)">  dengan nilai R (koefisien korelasi) sebesar 0,84278 dan simulasi prediksi menghasilkan MSE sebesar 1,632337 pada data normalisasi.Kata Kunci –    Kasus COVID-19, Metode Backpropagation, Metode Fuzzy Tsukamoto, Prediksi kasus COVID-19.