Klasifikasi Citra Emosi Wajah Menggunakan Convolutional Neural Network Untuk Penderita Depresi - Hariyanto; Novianti Puspitasari; Anindita Septiarini (Ikatan Ahli Informatika Indonesia, 2025)
Facial analysis is widely used as information to determine a person's psychological condition, such as depression. Someone suffering from depression tends to have a face that looks sad, empty, or unhappy. The appearance of a depressed person's face is almost similar to that of someone experiencing sadness. However, facial appearance is not always perceived as depressed, so facial emotion recognition is needed for depression treatment. A Convolutional Neural Network (CNN) is often used in image processing to identify key features and patterns in images, particularly for facial emotion recognition. CNN can be used to learn the relationship between facial shape and related emotions. This study employs the CNN method to classify facial emotions from facial expression images collected from a dataset of 30,724 images. The training process uses seven classes: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The accuracy results obtained a value of 67% with a training dataset of 21,507 images, a validation dataset of 6,143 images, and a testing dataset of 3,080 images.