Face Recognition with Backpropagation Artificial Neural Networks Using the Sigmoid Activation Function

Restu Monika Nia Betaubun (Department Ilmu Sosial, Hukum dan Humaniora, FHISIP, Universitas Terbuka)
Elvis Pawan (Department Informatics, STIMIK Sepuluh Nopember Jayapura)
Endang Yuliasih (Department UPBJJ-UT Jayapura, Universitas Terbuka)

International Journal of Computer and Information System (IJCIS)
Vol 3, No 3 (2022): IJCIS : Vol 3 - Issue 3 - 2022

In the current development of information technology facilities that are so rapid and complex, the reliability of a system that processes data properly will produce good information. The use of computers today is not only to help human work, but has also replaced human work in various fields. The purpose of this research is to design an image model in an application program. This existing digital image can then be used for various purposes. One way is to take photos on a driver's license (SIM), when taking photos directly using a digital camera and facial photo files are also directly stored in the police database. So this facial photo recognition program can actually also be applied by the police to recognize the faces of criminals. From the 10 experimental samples, it can be concluded that the best LearnRates with the lowest ErrorLevel for BMP format files are for learnrate 0.2 with an error rate of 18,54229 and a match rate of 81,45771 with a recognition rate of more than 60% in photo 2, while with an error rate of 51.00543 and a match level 48,99457