Klasifikasi Gender Melalui Citra Mata Menggunakan Metode Convolutional Neural Network (CNN) dengan Model Arsitektur VGG-16

Gender is often understood as a term that refers to a person's sex and has a grammatical sense as a grouping of words related to sex or neutrality. Moreover, gender also includes differences in behavior, roles, functions, and responsibilities between men and women that are determined by social conventions. Gender identification can be done by looking at physical characteristics such as eye imagery. In this study, eye images are used to identify gender using the Convolutional Neural Network (CNN) method, a subset of deep learning algorithms designed to process visual information. The VGG-16 architecture model was used to classify gender from a dataset consisting of 11,525 eye images, 6,323 male eye images and 5,202 female eye images. The results showed that the CNN method with the VGG-16 architecture model achieved an accuracy rate of 88.83% in gender classification based on eyes.