KLASIFIKASI CITRA WAJAH BERDASARKAN PENGGUNAAN KACAMATA MENGGUNAKAN ALGORITMA CNN DAN IMPLEMENTASI FLASK - Rahayu Fathan Asri; Subektiningsih Subektiningsih (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Face image classification is an important branch of computer vision and artificial intelligence, commonly applied in various fields such as facial recognition and facial attribute analysis. One facial attribute that is particularly interesting to classify is the use of eyeglasses, as it can affect the overall accuracy of facial recognition systems. This study aims to develop an eyeglass-use classification system based on a Convolutional Neural Network (CNN) and implement it in a web application using Flask to enable real-time prediction results. The research methodology includes collecting facial image datasets from Kaggle, performing preprocessing steps such as resizing, augmentation, and normalization, designing the CNN architecture, training the model, and evaluating its performance using a confusion matrix and classification report. The designed CNN model consists of three convolutional layers, max pooling, a flattening process, two fully connected layers, and a dropout layer to reduce the risk of overfitting. During the training phase, the model achieved 90% training accuracy and 96% validation accuracy, while testing on the test dataset resulted in an overall accuracy of 82%. The Flask-based system is capable of displaying real-time predictions, including the input image, classification label, accuracy percentage, and inference time. In the detection process, the accuracy of the model implementation reached 93% and the time required was in the range of a few milliseconds. The results demonstrate that the CNN can effectively classify faces with and without eyeglasses, and its implementation through a web interface offers broad potential for visual identification applications.