<![CDATA[The skin is an important organ that protects the human body, so early treatment is essential to prevent diseases such as skin cancer. Skin cancer is a serious disease that can be fatal ana requires high treatment costs. It ranks thirds after cervical cancer and breast cancer in Indonesia, with causative factors including genetics and exposure to UV radiation. Early detection and proper diagnosis are essential to increase the chances of recovery, so skin cancer classification is necessary to avoid delays in treatment. Deep Learning methods, particularly Convolutional Neural Network (CNN), have been shown to provide significant result in image classification with high accuracy. VGG16 and DenseNet121 are two popular CNN architecture used in image classification. This study aims to compare the performance of skin cancer classification using VGG16 and DenseNet121. The result show that the DenseNet121 architecture provides higher accuracy compared to the VGG16 architecture, with 93% accuracy for train data and 79% for test data, while the VGG16 architecture achieves 80% accuracy for train data and 74% for test data. These results show that the DenseNet121 architecture is superior in skin cancer classification, providing important information for more accurate diagnosis]]>
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