Deteksi Kesalahan Pengucapan Huruf Jawa Carakan dengan Jaringan Syaraf Tiruan Perambatan Balik

JK Aditya Christya Buditama(1*), Catur Atmaji(2), Agfianto Eko Putra(3)

(1) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author


Javanese is an Indonesian culture which needs to be preserved, but many Javanese students make mistakes in the pronunciation of Javanese letters and find it difficult to analyze errors by human teachers because of the limited time and subjective assessment, so a system is needed to detect incorrect pronunciation of Javanese letters. Mispronunciation detection system has been widely applied in foreign languages, but the system has not been implemented for Javanese carakan letters. This research develops the Javanese letters mispronunciation detection system using Back-Propagation Artificial Neural Networks (BP-ANN). The dataset is obtained from the recorded pronunciation of hanacaraka texts by 24 speakers  with 5 repetitions. ALNS method then used to automatically segment the signal into syllables. ANN-PB use statistical value of Mel-Frequency Cepstral Coefficient (MFCC) method with 7 and 14 coefficients. 10-Fold Cross Validation is used to validate and test the system. The Javanese mispronunciation detection using 7MFCC coefficients produces the highest accuracy of 80,07%. While the Javanese mispronunciation detection using 14 MFCC coefficients produces an accuracy of 82.36% at the highest.


MFCC; BP-ANN; Mispronunciartion; Javanese letters

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Tondo, F.H., 2009, Kepunahan Bahasa-bahasa Daerah, 11 (10), 277–296 [Online]. Available: [Accessed: 14-Okt-2018]

G. Huang, J. Ye, Z. Sun, Y. Zhou, Y. Shen and R. Mo, English mispronunciation detection based on improved GOP methods for Chinese students, 2017 International Conference on Progress in Informatics and Computing (PIC), Nanjing, 2017, pp. 425-429.

X. Li, J. Chen, M. Yao, D. Shen and F. Lin, English sentence pronunciation evaluation using rhythm and intonation, The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014), Shanghai, 2014, pp. 366-371.

T. Dang and Kim-Giao Dang Thi, Automatic detection of common mispronunciations of Vietnamese speakers of English using SVMs, 2017 International Conference on System Science and Engineering (ICSSE), Ho Chi Minh City, 2017, pp. 231-234.s

J. Yuan and M. Liberman, Automatic detection of “g-dropping” in American English using forced alignment, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, Waikoloa, HI, 2011, pp. 490-493.

L. Zhang, Research on English Pronunciation Recognition Based on Neural Network, 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xiamen, 2018, pp. 703-706.

K. Yoshida, T. Nose and A. Ito, Analysis of English Pronunciation of Singing Voices Sung by Japanese Speakers, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, 2014, pp. 554-557.

C. K. Dewa, Javanese vowels sound classification with convolutional neural network, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, 2016, pp. 123-128.

Suyanto & Putra, Agfianto. (2014). Automatic Segmentation of Indonesian Speech into Syllables using Fuzzy Smoothed Energy Contour with Local Normalization, Splitting, and Assimilation. Journal of ICT Research and Applications. 8. 97-112. 10.5614/itbj.ict.res.appl.2014.8.2.2.

A. Charisma, M. R. Hidayat and Y. B. Zainal, Speaker recognition using mel-frequency cepstrum coefficients and sum square error, 2017 3rd International Conference on Wireless and Telematics (ICWT), Palembang, 2017, pp. 160-163.

Sengupta, Nandini & Sahidullah, Md & Saha, Goutam. (2016). Lung sound classification using cepstral-based statistical features. Computers in Biology and Medicine. 75. 10.1016/j.compbiomed.2016.05.013.


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