Implementasi MobileNetV2 untuk Klasifikasi Jenis Acne Vulgaris pada Platform Website - Azwa Guntara; Egi Mutiara Br Sitepu (Jurnal Teknologi Informasi, 2026)
Acne vulgaris is one of the most common skin diseases affecting adolescents and adults. The identification of acne types is generally performed through visual examination by medical professionals. However, limited access to dermatology services and the high cost of consultation often lead many individuals to perform self-treatment without knowing the type of acne they have. This study aims to develop a web-based Acne Vulgaris classification system using a CNN with the MobileNetV2 architecture. The dataset used in this study was obtained from Kaggle and consists of 4,617 images divided into five classes: Blackheads, Cyst, Papules, Pustules, and Whiteheads. The dataset was split into 60% training data, 20% validation data, and 20% testing data. The preprocessing stage included image resizing to 224×224 pixels, data normalization, and image augmentation. The model was developed using transfer learning and fine-tuning on the last 30 layers of MobileNetV2. The training process employed the Adam optimizer with a learning rate of 0.00001, a batch size of 32, and 50 epochs. The results showed that the fine-tuned MobileNetV2 model achieved an accuracy of 79.96%, outperforming the transfer learning model, which achieved an accuracy of 75.27%. The model also obtained a precision of 81%, a recall of 80%, and an F1-score of 80%. The best-performing model was then implemented into a web-based application using the Flask framework, enabling automatic classification of Acne Vulgaris based on images uploaded by users.