Comparison and analysis of the artificial neural network method and SIRD on Covid-19 cases in Surabaya

Abstract

Since the first case of Covid-19, it is evidence that the spread of the disease in Indonesia is unavoidable. The virus continues to be widespread in Indonesian cities, one of which is Surabaya. Surabaya attained the 'crimson zone' status on June 2, 2020 due to the drastic increase of positive Covid-19 cases which tallies to 2748 people. The rapid pace at which Covid-19 spreads can result in a high death rate. This research tries to prevent high casualty rates by predicting the need for health equipment, isolation rooms, medical personnel, and the need for Personal Protective Equipment (PPE) for Covid-19 patients. There are two methods used for the sake of predicting, namely the Artificial Neural Network (ANN) and Susceptible Infectious Recovered Decease (SIRD) methods. The methods being mentioned will have their accuracies tested using error measurement methods which include the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). After measurements have been made, the prediction results from these 2 methods will be utilized to calculate the needs for equipment, isolation rooms, medical personnel, and PPE needs based on the regulatory patterns owned by the S, N, and X hospitals. Based on the results of the website implementation analysis, the ANN method is shown to have average error rates of 68,7467 for training and 75,4533 for testing based on the MAD method, 12487,67 for training and 13957,9267 for testing based on the MSE method, and 19,57% for training and 17,6% for testing based on the MAPE method. The SIRD method is shown to have average error rates of 551,1533, 1072639,5567, and 26,3033% for the MAD, MSE, and MAPE methods respectively.