Jaringan Saraf Tiruan Resilient Backpropagation untuk Memprediksi Faktor Dominan Injury Severity pada Kecelakaan Lalu Lintas
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Institusion
Universitas Sumatera Utara
Author
Yunita, Tika (STUDENT ID : 071402008)
(LECTURER ID : 0013085903)
(LECTURER ID : 0029018304)
(LECTURER ID : 0013085903)
(LECTURER ID : 0029018304)
Subject
Injury Severity
Datestamp
2022-12-14 08:38:58
Abstract :
Traffic accidents is an event that often occure around us. Traffic accident cause a variety of risks. The risks of accidents experienced by each person is different in each event. It can be divided into several categories of risk of traffic accidents or commonly known as the injury severity. In this study explains about the application of the method Resilient Backpropagation Neural Network to predict the severity of injury. This method is used to avoid a small gradient changes during the update process with Sigmoid activation function that causes the formation of a slow network. The result of this paper are resulting learning process more faster and better to predict the dominant factor of injury severity of traffic accidents.
Traffic accidents is an event that often occure around us. Traffic accident cause a variety of risks. The risks of accidents experienced by each person is different in each event. It can be divided into several categories of risk of traffic accidents or commonly known as the injury severity. In this study explains about the application of the method Resilient Backpropagation Neural Network to predict the severity of injury. This method is used to avoid a small gradient changes during the update process with Sigmoid activation function that causes the formation of a slow network. The result of this paper are resulting learning process more faster and better to predict the dominant factor of injury severity of traffic accidents.
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