Ear Identification Using Convolution Neural Network - Nadiyah Hidayati; Mawadatul Maulidah; Elin Panca Saputra (Jurnal Mantik, 2022)
Today's identification system has become a necessity for system security. One method of identification system that has a high level of security and accuracy is biometrics. Biometrics uses parts of the human body that are considered unique and can differentiate between one individual and another. One of the new biometrics that has become a concern in the world of research on biometrics is the ear. Ears have several advantages that other biometrics do not have, one of which is that they are not affected by changes in age. The purpose of this study was to determine the accuracy of the Convolutional Neural Network (CNN) algorithm in identifying ear images. CNN is currently one of the most superior algorithms in the field of object classification and identification. In this study, the ears that will be identified are images taken from the Kaggle dataset of 780 ears from 13 individuals with 60 images for each individual. This study resulted in a training accuracy of 96,3% and a testing accuracy of 79,7%.