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Abstract

Detection of Covid-19 using X-ray image data has advantages, namely, an affordable price with a high sensitivity value when compared to PCR, Swab, Antigen, and Genose tests. Therefore, this study aims to detect whether a patient is exposed to Covid-19 or not, based on lung X-ray image data using the Gray-Level Co-occurrence matrix (GLCM) feature extraction and the detection method using the Support Vector Machine (SVM). The evaluation model used in this study is the confusion matrix. This study uses 600 lung X-ray image data, consisting of 300 Covid-19 data and 300 normal data. The trials carried out in this study were using four angle orientations, namely 0°, 45°, 90°, and 135°. In addition, three types of kernels were tested, namely linear, polynomial, and RBF kernels. Data sharing uses k-fold cross validation with k=10. The best results were obtained from trials at 45° and 90° angle orientations using a polynomial kernel. The results of sensitivity, accuracy, and specificity obtained have the same value, namely 96.7% respectively. This system has an error of 3.3%. Where there is one normal Covid-19 data detected and one normal Covid-19 data detected, because the accuracy value exceeds 90.0%, it is concluded that the system built is good in detecting Covid-19 disease.