Abstract
The emergence of disease is inevitable in Indonesia and throughout the world. Glioblastoma which is one of the most common types of brain tumor is a dangerous disease that leads to death. Patients with this disease have a fairly low survival rate and are generally diagnosed when the tumor has developed further. Therefore, it is essential to make an early diagnosis with accurate results to determine the status of a person who has glioblastoma. In this study, the implementation of machine learning methods, namely K-Nearest Neighbor and Support Vector Machine with Genetic Algorithm as a feature selection (KNN-GA and SVM-GA) were compared to classify glioblastoma. Furthermore, the Genetic Algorithm (GA) was implemented to determine the selected relevant features and classified them by KNN and SVM methods. The numerical data used were obtained from Magnetic Resonance Imaging (MRI) as the results from Dr. Cipto Mangunkusumo Hospital. The results showed that the SVM-GA method using a Radial Basis Function kernel and 5 features with 90% training data was the best for classifying glioblastoma. The obtained values for accuracy, recall, precision, and f1-score were 92.35%, 93.19%, 92.62%, and 92.83% respectively.
Published Version
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