Prediksi Jumlah Penderita COVID-19 di Kota Malang Menggunakan Jaringan Syaraf Tiruan Backpropagation dan Metode Conjugate Gradient
Keywords:
backpropagation, conjugate gradient, jaringan syaraf tiruan, prediksi, penderita COVID-19Abstract
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.
Downloads
References
Ibrahim, I. M., Abdelmalek, D. H., Elshahat, M. E., & Elfiky, COVID-19 Spike-host Cell Receptor GRP78 Binding Site Prediction 80, Journal of Infection,(5), 554–562. (2020).
Pradanti, Evaluation of Formal Risk Assessment Implementation of Middle East Respiratory Syndrome Coronavirus in 2018, Jurnal Berkala Epidemologi, 7(3), 197. (2019).
Barda, N., Riesel, D., Developing a COVID-19 Mortality Risk Prediction Model when Individuallevel Data are not Available, Nature Communications, 11(1), 1–9. (2020)
Kavadi, D. P., Patan, R., Ramachandran, M., & Gandomi, Partial Derivative Nonlinear Global Pandemic Machine Learning Prediction of COVID 19, 139. (2020).
Wynants, L., Van Calster, B., Prediction Models for Diagnosis and Prognosis of Covid-19: Systematic Review and Critical Appraisal, Chaos, Solitons, and Fractals, 369. (2020).
Yi, Y., Lagniton, P. N. P., Ye, S., Li, E., & Xu, COVID-19: What has been Learned and to be Learned about the Novel Coronavirus Disease., International Journal of Biological Sciences, 16(10), 1753–1766. (2020).
Dona, Finky, Komparasi Algoritma Conjugate gradient dan Gradient Descent pada MLPNN untuk Tingkat Pengetahuan Ibu, Prosiding Konferensi Nasional Sistem & Informatika, 507–512. (2017).
Bafitlhile, T. M., Li, Z., & Li, Q, Comparison of Levenberg Marquardt and Conjugate Gradient Descent Optimization Methods for Simulation of Streamflow Using Artificial Neural Network, Advances in Ecology and Environmental Research, 3(2517–9454), 217–237. (2018).
Thaheer, H, Teknik Optimasi Lanjut, Denpasar: Udayana University Press. (2019).
Du, K. L., & Swamy, Neural Networks in a Softcomputing Framework, 1–566. (2006).
Sudarsono, A. Jaringan Syaraf Tiruan untuk Memprediksi Laju Pertumbuhan Penduduk Menggunakan Metode Backpropagation, Media Infotama, 61–69 . (2016).
Laurene, F., Fundamentals of Neural Network, Architectures, Algorithm and Applications. United State: Prentice Hall, inc. (1994).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Prosiding Seminar Nasional Teknoka
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Supported by :
