<![CDATA[Smallpox is a human skin disease that causes fluid-filled lumps, especially on the face and can spread throughout the body. There are several types of smallpox, including chickenpox (caused by the Varicella-Zoster virus), monkeypox (by the monkeypox virus), and cowpox (by the cowpox virus). Although smallpox is often considered mild, it can cause serious complications, especially for people with weakened immune systems. This research aims to develop an application that uses the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture to help doctors diagnose types of smallpox. CNNs work similarly to the way the human brain recognizes objects in images. The dataset used consists of 3200 images 800 images for each type of smallpox and healthy skim, with 80% for training data and 20% for test data. The test results show the highest accuracy of 92%, using Batch size 16, Learning rate 0.0001, Optimizer Adam, and Epoch 40.]]>
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