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Over the past few years, the diversity of frogs has declined rapidly due to several problems, namely lost habitat and environmental pollution. So, identifying frog is very important because frog have an important role in the food chain. In this study there are several stages to classify the sound of frog based on fractal dimension values ​​including the pre-processing, feature extraction, calculation of value fractal dimension, and classification. There are two stages in pre-processing including filtering and normalization process. For the feature extraction process used the Discrete Wavelet Transform (DWT) method to decomposition frog sound. after feature extraction processes that have time domains are changed into the frequency domain using Fast Fourier Transform (FFT). And the result of FFT is calculated by the fractal dimension value using Higuchis method. The next process is classification used K-Nearest Neighbor (K-NN) method. Before carrying out the classification process, the data distribution process from the value fractal dimension ​​using the k-fold cross validation with a value of  k = 5. From the 5 K values ​​used the best accuracy value was obtained at 81.25% for K = 5 and K-Max = 16.

Anura; frog voice signal; Higuchi fractal dimension, K-NN, DWT