<span id="docs-internal-guid-e57881bf-7fff-62db-2c1e-192664c8e8a8"><span>Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amount of 113 data and 5 features. The method that used is support vector machines (SVM) and random forest method. SVM is the classification method that uses discriminant hyper-plane, dividing to classes. meanwhile, random forest is a tree-based ensemble depending on a collection of random variables. SVM and random forest (RF) are applied to predict hepatitis data, and then the results will be compared.</span></span>
<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>
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