Prediction Active Case of Covid-19 with ERNN - Aprianti, Winda; Permadi, Jaka; Rhomadhona, Herfia (JTAM (Jurnal Teori dan Aplikasi Matematika), 2022)
SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.