期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：2
页码：788-793
DOI：10.12928/telkomnika.v17i2.9547
出版社：Universitas Ahmad Dahlan
摘要：This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature
extraction method of the first order for feature extraction based on texture in order to get high degree of
accuracy with method of classification using artificial neural network (ANN). The method used is training
and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed
that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma
with the lowest accuracy of 80% and the highest accuracy of 88,88%, respectively. The 4 sets of training
used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of
melanoma images and 17 as suspected melanoma images.
其他摘要：This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
