Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya

Zuraidah Fitriah (Jurusan Matematika, Universitas Brawijaya, Indonesia)
Mohamad Handri Tuloli (Jurusan Matematika, Universitas Brawijaya, Indonesia)
Syaiful Anam (Jurusan Matematika, Universitas Brawijaya, Indonesia)
Noor Hidayat (Jurusan Matematika, Universitas Brawijaya, Indonesia)
Indah Yanti (Jurusan Matematika, Universitas Brawijaya, Indonesia)
Dwi Mifta Mahanani (Jurusan Matematika, Universitas Brawijaya, Indonesia)

Telematika : Jurnal Informatika dan Teknologi Informasi
Vol 18, No 2 (2021): Edisi Juni 2021

Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.