BibTex Citation Data :
@article{Transmisi3647, author = {Angga Setiawan dan Achmad Hidayatno dan R. Rizal Isnanto}, title = {Aplikasi Pengenalan Ucapan dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) untuk Mengoperasikan Kursor Komputer}, journal = {Transmisi: Jurnal Ilmiah Teknik Elektro}, volume = {13}, number = {3}, year = {2012}, keywords = {}, abstract = { During this time, computer cursor operation was done by pressing and moving the mouse. So, this is less flexible for computer user that require movement in operating a computer, since to use mouse comfortably someone has to sit. Moreover, physical completeness is required for mouse operating, so that for someone who has physical disabilities feels difficult to operate it. Therefore, it is required to develop a system that provides a better comfort and flexibility not only for the healthy user computer but also for the user computer who has physical disabilities. In this final project, computer cursor operation program via voice is created. With this program, someone will have more flexibility when operating the computer cursor and also people with physical disabilities is enabled to communicate with computer. Voice recognition is a technology that is apllied in this program, with the feature extraction process used MFCC (Mel-Frequency Cepstrum Coefficients) method. As for the recognitions process used artificial neural network type LVQ (Learning Vector Quantization). Voice is passed through a microphone and then it is analyzed by MFCC to produce MFCC coefficients. These coefficients are used as input vector for LVQ neural network and used as data to train the network until it has the classification capability. Programming language that is used in creating this software is Delphi programming language. Based on the result of the testing program, it is found that the success percentage rate of voice recognition with training data, that is data which is derived from databases that have been recorded and trained into the program which amounts to 240 data, is 88,89 %. While in the testing with test data, that is data which is derived from the real time sayings of respondents which is amounts to 240 data, it is found that the success percentage rate of voice recognition is 83,99 %. Keyword : voice recognition, computer cursor, MFCC, LVQ }, issn = {2407-6422}, pages = {82--86} doi = {10.12777/transmisi.13.3.82-86}, url = {https://ejournal.undip.ac.id/index.php/transmisi/article/view/3647} }
Refworks Citation Data :
During this time, computer cursor operation was done by pressing and moving the mouse. So, this is less flexible for computer user that require movement in operating a computer, since to use mouse comfortably someone has to sit. Moreover, physical completeness is required for mouse operating, so that for someone who has physical disabilities feels difficult to operate it. Therefore, it is required to develop a system that provides a better comfort and flexibility not only for the healthy user computer but also for the user computer who has physical disabilities. In this final project, computer cursor operation program via voice is created. With this program, someone will have more flexibility when operating the computer cursor and also people with physical disabilities is enabled to communicate with computer. Voice recognition is a technology that is apllied in this program, with the feature extraction process used MFCC (Mel-Frequency Cepstrum Coefficients) method. As for the recognitions process used artificial neural network type LVQ (Learning Vector Quantization). Voice is passed through a microphone and then it is analyzed by MFCC to produce MFCC coefficients. These coefficients are used as input vector for LVQ neural network and used as data to train the network until it has the classification capability. Programming language that is used in creating this software is Delphi programming language. Based on the result of the testing program, it is found that the success percentage rate of voice recognition with training data, that is data which is derived from databases that have been recorded and trained into the program which amounts to 240 data, is 88,89 %. While in the testing with test data, that is data which is derived from the real time sayings of respondents which is amounts to 240 data, it is found that the success percentage rate of voice recognition is 83,99 %.
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Transmisi: Jurnal Ilmiah Teknik Elektro dan Departemen Teknik Elektro, Universitas Diponegoro dan Editor berusaha keras untuk memastikan bahwa tidak ada data, pendapat, atau pernyataan yang salah atau menyesatkan dipublikasikan di jurnal. Dengan cara apa pun, isi artikel dan iklan yang diterbitkan dalam Transmisi: Jurnal Ilmiah Teknik Elektro adalah tanggung jawab tunggal dan eksklusif masing-masing penulis dan pengiklan.
Formulir Transfer Hak Cipta dapat diunduh di sini: [Formulir Transfer Hak Cipta Transmisi]. Formulir hak cipta harus ditandatangani dan dikirim ke Editor dalam bentuk surat asli, dokumen pindaian atau faks:
Dr. Munawar Riyadi (Ketua Editor)Departemen Teknik Elektro, Universitas Diponegoro, IndonesiaJl. Prof. Sudharto, Tembalang, Semarang 50275 IndonesiaTelepon/Facs: 62-24-7460057Email: transmisi@elektro.undip.ac.id
