<![CDATA[Skin health is a vital aspect as it functions as the body's primary protector from the external environment. Various skin diseases can arise due to infections, allergies, autoimmune disorders, or environmental factors, and often exhibit similar symptoms, making diagnosis difficult. Artificial intelligence technology, such as Artificial Neural Networks (ANN), offers an innovative solution for accurate diagnosis. One popular ANN method is Backpropagation, which updates network weights iteratively based on the errors produced. This research focuses on applying the Backpropagation algorithm to diagnose skin diseases based on patient symptoms. With a binary data-based system and training using Backpropagation, this system is expected to accurately map symptoms to types of skin diseases. The methodology involves problem identification , data collection (types of skin diseases and symptoms, encoded in binary), dataset and diagnosis rule formation , ANN design (input, hidden, and output layers) , and training and testing using binary data and one-hot encoding. The results indicate that the application of ANN with Backpropagation is effective in assisting the automatic diagnosis process for skin disease cases , achieving an accuracy of 90%. This demonstrates the significant potential of this method in automated medical expert systems.]]>
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