Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction

Ery Shandy; Abdul Halim  Anshor; Dodit  Ardiatma

doi:10.47709/cnahpc.v6i3.4226

Authors

Ery Shandy Program Studi Teknik Informatika, Fakultas Teknik, Universitas Pelita Bangsa
Abdul Halim Anshor Program Studi Teknik Informatika, Fakultas Teknik, Universitas Pelita Bangsa
Dodit Ardiatma Program Studi Teknik Lingkungan, Fakultas Teknik, Universitas Pelita Bangsa

DOI:

10.47709/cnahpc.v6i3.4226

Keywords:

K-Nearest Neighbor(K-NN), Data Mining, MFCC, Classification, Sundanese

Dimension Badge Record

Abstract

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.

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