<![CDATA[Brain tumors are a deadly disease that can attack anyone without knowing age. Technology that continues to grow makes the world of health inseparable from technology. One of the technologies used to identify a disease is CT Scan and MRI. Clinically it is difficult to distinguish between benign and malignant brain tumors because like normal brain tissue, doctors can diagnose the disease without having to do surgery. This study aims to detect benign and malignant brain tumors by using extraction of GLCM and SVM features. GLCM is one method for obtaining statistical characteristics by calculating the probability of the neighboring relationship between two pixels at a certain distance and angle orientation. While the SVM method is due to the best class and classification separation and is able to work on high-dimensional datasets. The ct-scan image that is entered will be segmented which will later be extracted using GLCM features, the features used include mean, contrast, correlation, homogeneity, IDM, variance and entropy. After testing, it can be concluded that the accuracy rate is 94.5%. While using the WEKA application is 91.6666% and an error of 8.3334%. Keywords : Classification, Ct- Scan and MRI, brain tumor, GLCM, SVM]]>
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