Prediction of COVID-19 Using Recurrent Neural Network Model - Alamsyah, Alamsyah; Prasetiyo, Budi; Hakim, M. Faris Al; Pradana, Fadli Dony (Scientific Journal of Informatics, 2021)
Purpose: The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. Methods: In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. Result: The research results show the percentage of accuracy is 88. Novelty: One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN).