<![CDATA[Skin cancer is one type of cancer that is quite serious that can not be controlled completely, so that many still result in death, disability and high medical costs. The diagnosis process carried out by dermatologists generally uses the Biopi process which is expensive, painful and requires a long recovery time for the wound, due to taking body tissue that scratches a small piece of tissue or by using a syringe to get a sample. Therefore we need a tool or system that can speed up helping to find out the type of skin cancer suffered, so that it can find out its treatment early by using digital image processing techniques. The purpose of this study is to classify the types of skin cancer based on texture and color image features using the SVM and KNN algorithm. The benefits are expected to help the skin medicine team in diagnosing skin cancer early. The features used are grayscale imagery taken by the average value, standard deviation, skewness, entropy, variance, contrast, energy, correlation, and homogeneity. Furthermore, the value of these features is trained and classified. The classification results using the SVM algorithm have an accuracy value of 69.85%. And accuracy using the KNN algorithm, with a value of K = 2 67.27%, K = 3 67.88%, K = 4 70.15%, K = 5 70.61%, K = 6 69.55%. Thus the best K on KNN is 5, the accuracy is 70.61%. Where the data used are 2637 training dataset images, and 660 test data images. And classified as a class of malignant, benign skin cancer.]]>
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