• Made Agung Raharja Universitas Udayana
  • I Putu Gede Adiatmika Universitas Udayana
  • I Nyoman Adiputra Universitas Udayana
  • Susy Purnawati Universitas Udayana
  • I Wayan Supriana Universitas Udayana



Sekar Alit, Speech Recognition, MFCC


The province of Bali has various types of songs that have different structures and functions, one of which is the Sekar Alit song. Seeing current technological advances, conservation efforts should follow the development of existing technology. This study recognizes the singer's voice with the MeliFrequency iCepstrum Coefficients (MFCC) method used to perform feature extraction, i.e. to obtain a parameter and information regarding the characteristics of a person's voice and the training of voice pattern matching against the voice of the DTW Singer Alit (Dinamic) and the Time Warping Alit method. . So that the final result of this research is an android-based tembang sekar alit learning application that can be used by students to learn more easily and effectively which is called the SekARAI application. The results of usability testing on the application found that the average value was above 3, which means that the SekARAI software that has been implemented has met the Usability element and besides that the software is easy to use and understand by users. The Sekar Alit Song Voice Recognition application has been evaluated using the confusion matrix method, it is found that the MFCC algorithm test results in an accuracy of 76.6%.


Download data is not yet available.


I. W. Kotaniartha and A. Wijayanti, ‘MAKNA PESAN MORAL LIRIK LAGU TRADISIONAL BALI (SEKAR ALIT) DALAM MEMBENTUK KARAKTER ANAK’, in Seminar Nasional INOBALI 2019 Inovasi Baru dalam Penelitian Sains, Teknologi dan Humaniora, 2019, pp. 1178–1185.

I. D. G. B. D. Prabhawa, D. C. Khrisne, and M. Sudarma, ‘Rancang Bangun Aplikasi Pengenalan Pupuh Bali Menggunakan Metode Mel Frequency Cepstral Coefficients’, Ranc. Bangun Apl. Pengenalan Pupuh Bali Menggunakan Metod. Mel Freq. Cepstral Coefficients, vol. 3, no. 1, pp. 75–81, 2019, doi: 10.29303/jcosine.v3i1.237.

A. Setiawan and R. A. Widyanto, ‘Evaluasi Website Perguruan Tinggi menggunakan Metode Usability Testing’, J. Inform. J. Pengemb. IT, vol. 3, no. 3, pp. 295–299, Oct. 2018, doi: 10.30591/jpit.v3i3.912.

S. S. Zarish, ‘Analyzing Usability of Educational Websites Using Automated Tools. 2019 International Conference on Computer and Information Sciences (ICCIS)’, 2019 Int. Conf. Comput. Inf. Sci., pp. 1–4, 2019.

K. Al-omar, ‘Evaluating the Usability and Learnability of the “Blackboard” LMS Using SUS and Data Mining’, 2018 Second Int. Conf. Comput. Methodol. Commun., pp. 386–390, 2018.

J. Sauer, A. Sonderegger, K. Heyden, J. Biller, J. Klotz, and A. Uebelbacher, ‘Extra-laboratorial usability tests : An empirical comparison of remote and classical fi eld testing with lab testing’, Appl. Ergon., vol. 74, pp. 85–96, 2019.

M. A. Raharja, I. D. M. B. A. Darmawan, D. P. E. Nilakusumawati, and I. W. Supriana, ‘Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction’, J. Phys. Conf. Ser., vol. 1722, no. 1, 2021, doi: 10.1088/1742-6596/1722/1/012005.

M. N. Rabbani, A. Rizal, and F. Y. Suratman, ‘Implementasi Kunci Berbasis Suara Menggunakan Metode Mel Frequency Frequency Cepstral Coefficient (MFCC)’, vol. 3, no. 3, pp. 3998–4007, 2016.

C. Asmita, T. Savitha, and K. Upadhya, ‘Voice Recognition Using MFCC Algorithm’, 2014. Accessed: Mar. 23, 2021. [Online]. Available:

A. K. Santra and C. J. Christy, ‘Genetic Algorithm and Confusion Matrix for Document Clustering’, Int. J. Comput. Sci. Issues, vol. 9, no. 1, pp. 322–328, 2012.




How to Cite

Raharja, M. A. ., Adiatmika, I. P. G., Adiputra, I. N., Purnawati, S., & Supriana, I. W. (2022). IMPLEMENTASI METODE MEL-FREQUENCY CEPSTRAL COEFFICIENT DAN DTW PADA APLIKASI PENGENALAN SUARA TEMBANG SEKAR ALIT . Jurnal RESISTOR (Rekayasa Sistem Komputer), 5(1), 65-71.