<![CDATA[The lungs are an important organ in the human respiratory system, which functions to exchange carbon dioxide from the blood with oxygen in the air. Detection of respiratory disorders and lung disorders can be done in various ways; view medical records, physical examination, detection by x-ray and also auscultation of breathing. Digital signal processing can be used as a method to detect lung disorders based on the sound produced. In this study, lung sounds were classified into normal, crackle, wheeze, and crackle-wheeze classes using the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Network (CNN) methods.Observations were made by varying the MFCC feature extraction using MFCC 8 and 13 coefficients, the number of frames are 50 and 60, and the width of the frames used was 0,1, 0,15 and 0,2 seconds. The result of feature extraction is then applied to the CNN classification system, and the confusion matrix is used to get the accuracy and precision values. The highest accuracy and precision values were obtained at 71,85% and 65,70% on the MFCC 13 coefficient with an average of 71,18%. Based on these results, the system that has been created can classify normal lung sounds, crackle, wheeze and crackle-wheeze quite well.]]>
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