Prediksi Jumlah Penderita COVID-19 di Kota Malang Menggunakan Jaringan Syaraf Tiruan Backpropagation dan Metode Conjugate Gradient - Syaiful Anam; Mochamad Hakim Akbar Assidiq Maulana; Noor Hidayat; Indah Yanti; Zuraidah Fitriah; Dwi Mifta Mahanani (Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, 2020)
COVID-19 is an infectious disease caused by infection with a new type of corona virus. This disease is very dangerous and causes death, especially for sufferers who have congenital diseases or who have low immunity. The disease is spread through droplets from the nose or mouth that come out when a person infected with COVID-19 coughs, sneezes or talks. The prediction of the number of COVID-19 sufferers is very important to prevent and combat the spread of this disease. The backpropagation neural network is a method that can be used to solve predictive problems with good results, but its performance is influenced by the optimization method used during training. In general, the optimization method used is the gradient descent method, but this method has slow convergence. The Conjugate Gradient method has very good convergence when compared to the gradient descent method. In this paper, we will discuss how to make a prediction model for the number of COVID-19 sufferers in Malang using the backpropagation neural network and the conjugate gradient method. The experimental results show that the prediction model gets good results when compared to artificial neural networks that are optimized by the gradient descent method.