<![CDATA[Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.]]>
