<![CDATA[Song cannot be separated from humans daily activities. When listening to songs humans can focus more on their activities. The rapid development of information on multimedia and electronic devices has led to a dramatic increase in music appreciation and creation. On the one hand this increase encourages people to enjoy songs more. But on the other hand, this increase forced the development of new technologies for the convenience of listening to songs. An example is how someone wants to find a song based on a song that has been heard. Genres classification is one of machine learning techniques that can group songs based on their usefulness. This technique can be used as a function in a system to support other functions, such as song recommendations, special word, or similar song searches. This study will use the K-Nearest Neighbor (K-NN) method as a genre classification technique for songs. To measure the similarity of two songs, a normalized cross correlation (NCC) equation is used to replace the distance calculation equation in the K-NN method. The features that extracted from a song are zero crossing rate, spectral centroid, spectral rolloff, and energy. Data obtained from feature extraction will be normalized using the z-score equation. The test results show that the best evaluation is obtained when the duration is 10, the offset is 120, and K in K-NN is 10. Precision, recall, and f-measure that obtained in this study are precision with a value of 0.637, recall with a value of 0.633, and f-measure with a value of 0.635.]]>
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