Implementasi Ekstraksi Fitur GLCM dengan Klasifikasi Algoritma C5.0 Pada Data Computerized Tomography Scan Covid-19
Abstract
Digital imaging technology has been widely used in the medical field in the diagnosis of biological image data to guide doctors to determine the patient's condition. One of the medical imaging techniques that can describe conditions in the human body is Computed Tomography (CT). This study used a chest CT scan image dataset totaling 625 CT data. The feature extraction used to get some statistical features about the image is GLCM (Gray Level Co-Occurrence Matrix). In GLCM Distance is represented as pixels whereas orientation is represented in degrees. Orientation is formed from four angular directions with intervals of 0°, 45°, 90°, and 135°. While the distance between pixels is usually set at 1 pixel. After feature extraction, classification will be carried out using the C5.0 algorithm method. Accuracy results from the C5.0 classification method using GLCM feature extraction get results of an accuracy of 87% at an angle of 90°, 84% at an angle of 45°, 83% at an angle of 135° , and 82% at an angle of 0°.
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References
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