Perbandingan dan Analisis Metode Artificial Neural Network dan SIRD pada Kasus Covid-19 di Surabaya

Abstract

On March 2, 2020, President Joko Widodo's announcement regarding the COVID-19 virus has made its way to all of Indonesia, serving as a warning to the people. The virus continues to grow and spread its influence across cities, one of which is Surabaya. Surabaya attained the 'crimson zone' status on 2nd of June, 2020 due to the drastic increase of positive COVID-19 cases which tallies to 2748 people. The rapid pace at which COVID-19 spreads results in a high death rate.

This research was done to try and 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 were two methods used for the sake of predicting, namely the Artificial Neural Network (ANN) and Susceptible Infectious Recovered Decease (SIRD) methods. The methods in question 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 53.1733 for training and 89.73 for testing based on the MAD method, 6581.09 for training and 22953.9067 for testing based on the MSE method, and 17.7367% for training and 16.3067% for testing based on the MAPE method. The SIRD method is shown to have average error rates of 309.81, 150496.08, and 30.2% for the MAD, MSE, and MAPE methods respectively.