Klasifikasi Jenis Jerawat Berdasarkan Citra Menggunakan Convolutional Neural Network dengan Arsitektur MobileNetV2

Among teenagers and adults worldwide, acne ranks as one of the most frequent skin concerns where each type of acne requires specific treatments. The proposed method in this study classifies five types of acne using deep learning. This methodology utilizes CNN architecture to specifically categorize different types of acne. The model used in this research adopts the MobileNetV2 architecture with transfer learning, consisting of several main layers: MobileNetV2 as the base model, Global Average Pooling, Flatten, Dense Layer, Dropout, and Softmax classification. The validation of the architecture utilizes secondary data consisting of 350 images. This dataset covers five different types of acne: Acne Fulminans, Acne Nodules, Papule, Pustule, and Fungal Acne, with each class containing 70 different samples. Challenges in automating this process include image lighting variations, image quality and data limitations. The CNN classification system shows performance rates of 89% for training and 80% for testing. The results indicatee that the proposed architecture can classify acne types with a reasonably good accuracy level, although there is room for improvement in model generalization.