Klasifikasi Jenis Jerawat Secara Otomatis Dengan Convolutional Neural Network Menggunakan Arsitektur Resnet-50 - Anshori, Muhammad Iqbal; Zafar Sidiq, Muhammad Ali; Yaqin, Rifki Ainul; Prasetyo Agung, Ignatius Wiseto (Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia, 2025)
Acne is a common skin problem that requires different treatments based on its type, such as blackheads, conglobata, and papulopustular. This research develops an automatic acne type classification system using deep learning-based Residual Network (ResNet-50) architecture. With its 50 layers, ResNet-50 is effective in image classification. The objective of of this research is to classify the type of acne from skin images on the face, so that it can help diagnosis and treatment. face, so that it can help diagnosis and treatment. The method used in this research includes several main stages, namely the collection of the dataset, model training using CNN with ResNet-50 architecture, model testing, and performance evaluation. model, and performance evaluation. The dataset was obtained from Roboflow, consisting of three classes: acne-comedonica, acne-conglobata, and acne-papulopustulosa. The process involves image preprocessing, data augmentation, and model parameter adjustment, including Adam's dropout and optimizer techniques. The model can achieve 98.35% accuracy with loss of 0.0489 and the highest validation accuracy of 92.86% with a validation loss of 0.1976. In addition, confusion matrix analysis shows an accuracy result of 93%, which indicates the performance of the model in distinguishing between acne classes effectively. These results show that the model is effective in classifying the types of acne and can have a significant impact in assisting a more accurate and faster diagnosis. more accurate and quicker diagnosis.