Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.