Face Detection Dengan Model Arsitektur VGG 19 Pada Metode Convolutional Neural Network - Pramuditha, Adeyuni Zada; Suroso, Suroso; Fadhli, Mohammad Fadhli (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2024)
This study develops a facial emotion detection model using the VGG-19 architecture on the Convolutional Neural Network (CNN) method. The study aims to improve the accuracy of facial expression recognition in a variety of applications, including the fields of education and marketing. The dataset used consisted of 7 categories of emotions, with 80% of the training data and 20% of the testing data. The research process includes dataset collection, data pre-processing, CNN model design, and model training with epoch variations. The results showed an increase in accuracy as the number of epochs increased, with the highest accuracy reaching 69.81% in training data and 63.30% in validation data after 100 epochs. The model showed good performance in classifying