<![CDATA[Arrhythmia (heart rhythm disorder) is a disorder of the cardiac elektrophysology caused by disruption of the conduction system as well as impaired formation and delivery of electrical impulses. Some factors that influence arrhythmia include age, blood pressure, height and weight. This arrhythmia can be recognized by using a cardiac record or electrocardiogram (ECG). Numerical data generated by ECG has many features that are not easily processed manually. Computer assistance with certain machine learning techniques can be used to automatically recognize diseases. One method of machine learning is support vector machine (SVM). In this study, a system was designed to classify arrhythmias using support vector machine (SVM) methods. The most optimal accuracy value or accuracy that has the highest value indicates that the system is in accordance with expectations, so that the support vector machine method is able to measure the accuracy of the classification of arrhythmias based on electrocardiogram results with RBF kernel function of 92%.]]>
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