KLASIFIKASI GENDER BERDASARKAN SUARA DENGAN NAIVE BAYES DAN MEL FREQUENCY CEPSTRAL COEFFICIENT
Abstract
For humans, recognizing sounds is an easy thing, by listening carefully and understandingly to what is spoken and humans have intelligence in recognizing sound patterns. Unlike computers, the speech recognition process is a difficult process, this is because the computer requires a standard and logical mechanism to recognize sound patterns. With Mel Frequency Cepstral Coefficient (MFCC) method has an important role in determining the characteristics of a sound. This method is often used for verification of voice, speech recognition, emotion detection of voice. To perform the classification in this study using Naïve Bayes method. The Naive Bayes method is a classification method. In which the classification process in the naïve Bayes method is based on the probability of the data as evidence in probability. The model used in the Naive Bayes method is the independent attribute model. The accuracy rate in this research was 87%. It is based on the amount of data testing 100 samples, the true classified as 87 samples of data while false classified as 13 sample data.
Full Text:
PDFReferences
Charisma, A. (2013). Sistem Verifikasi Penutur Menggunakan Metode Mel Frequency Cepstral Coefficient-Vector Quantisation (MFCC-QV) Serta Sum Square Error (SSE) dan Pengenalan Kata Menggunakan Metode Logika Fuzzy. Jurnal Teknik Elektro, 2(2).
Essra, A., Rahmadani, & Safriadi. (2016). Analisis Information Gain Attribute Evaluation Untuk Klasifikasi Serangan. Journal of Information System Development, 2(2), 9–14.
Kamarulafizam, I., Salleh, S. H., Najeb, J. M., Ariff, A. K., & Chowdhury, A. (2007). Heart sound analysis using MFCC and time frequency distribution. IFMBE Proceedings, 14(1), 946–949. https://doi.org/10.1007/978-3-540-36841-0_225
Lalitha, S., Geyasruti, D., Narayanan, R., & Shravani, M. (2015). Emotion Detection Using MFCC and Cepstrum Features. Procedia Computer Science, 70, 29–35. https://doi.org/10.1016/j.procs.2015.10.020
Mursyidah, M. (2017). Pengenalan Karakter Suara Laki-Laki Aceh Menggunakan Metode FFT (Fast Fourier Transform). Jurnal Infomedia, 2(1), 20–24. https://doi.org/10.30811/.v2i1.463
Mustafa, M. S., Ramadhan, M. R., & Thenata, A. P. (2018). Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier. Creative Information Technology Journal, 4(2), 151. https://doi.org/10.24076/citec.2017v4i2.106
Nasution, T. (2012). Metoda Mel Frequency Cepstrum Coefficients (MFCC) untuk Mengenali Ucapan pada Bahasa Indonesia. SATIN - Sains Dan Teknologi Informasi.
Nurhamidah, N., Djamal, E. C., & Ilyas, R. (2017). Perintah Menggunakan Sinyal Suara dengan Mel- Frequency Cepstrum Coefficients dan Learning Vector Quantization. Seminar Nasional Aplikasi Teknologi Informasi (SNATi) 2017, 11–16.
Permana, I. S., Indrawaty, Y., & Zulkarnain, A. (2019). Implementasi Metode Mfcc Dan Dtw Untuk Pengenalan Jenis Suara Pria Dan Wanita. MIND Journal, 3(1), 61–76. https://doi.org/10.26760/mindjournal.v3i1.61-76
Prithvi, P., & Kumar, T. K. (2016). Comparative Analysis of MFCC, LFCC, RASTA-PLP. International Journal of Scientific Engineering and Research, 4(5), 1–4.
Riyani, A., Nurrochman, A., Sanjaya, E., Rizqiyah, P., & Junaidi, A. (2019). Mengidentifikasi Sinyal Suara Manusia Menggunakan Metode Fast Fourier Transform (Fft) Berbasis Matlab. Inista, 1(2), 42–50.
Setiawan, Angga, Hidayatno, A., & Isnanto, R. R. (2011). Aplikasi Pengenalan Ucapan dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) untuk Mengoperasikan Kursor Komputer. Aplikasi Pengenalan Ucapan Dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) Untuk Mengoperasikan Kursor Komputer, 13(3), 82–86. https://doi.org/10.12777/transmisi.13.3.82-86
Setiawan, Arif, & Handayani, P. K. (2012). Klastering Suara Berdasatkan Gender dengan Ekstraksi Ciri Berbasis Domain Waktu. Seminar Nasional Teknologi Informasi Dan Komunikasi Terapan 2012, 2012(Semantik), 364–370.
DOI: https://doi.org/10.38038/vocatech.v2i1.45
Refbacks
- There are currently no refbacks.
VOCATECH : Vocational and Technology Journal
Unit Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu
Akademi Komunitas Negeri Aceh Barat
Komplek STTU Alue Peunyareng, Ujong Tanoh Darat, Meureubo, Kabupaten Aceh Barat, Aceh 23615
Telp. (0655) 7110271
Email: vocatech@aknacehbarat.ac.id
VOCATECH: Vocational Education and Technology Journal Published by:
Unit Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu
Akademi Komunitas Negeri Aceh Barat
Indexed by:
VOCATECH: Vocational Education and Technology Journal Creative Commons Attribution-ShareAlike 4.0 International License.