Informatics Department, Universitas Diponegoro, Indonesia
BibTex Citation Data :
@article{JMASIF34874, author = {Panggih Tridarma and Sukmawati Endah}, title = {Pengenalan Ucapan Bahasa Indonesia Menggunakan MFCC dan Recurrent Neural Network}, journal = {Jurnal Masyarakat Informatika}, volume = {11}, number = {2}, year = {2020}, keywords = {Pengenalan ucapan, Mel-Frequency Cepstral Coefficient, Recurrent Neural Network, Elman RNN, Jordan RNN}, abstract = {Pengenalan ucapan ( speech recognition ) merupakan perkembangan teknologi dalam bidang suara. Pengenalan ucapan memungkinkan suatu perangkat lunak mengenali kata-kata yang diucapkan oleh manusia dan ditampilkan dalam bentuk tulisan. Namun masih terdapat masalah untuk mengenali kata-kata yang diucapkan, seperti karakteristik suara yang berbeda, usia, kesehatan, dan jenis kelamin. Penelitian ini membahas pengenalan ucapan bahasa Indonesia dengan menggunakan Mel-Frequency Cepstral Coefficient (MFCC) sebagai metode ekstraksi ciri dan Recurrent Neural Network (RNN) sebagai metode pengenalannya dengan membandingkan arsitektur Elman RNN dan arsitektur Jordan RNN. Pembagian data latih dan data uji dilakukan dengan menggunakan metode k-fold cross validation dengan nilai k=5. Hasil penelitian menunjukkan bahwa arsitektur Elman RNN pada parameter 900 hidden neuron , target error 0.0005, learning rate 0.01, dan maksimal epoch 10000 dengan koefisien MFCC 20 menghasilkan akurasi terbaik sebesar 72.65%. Sedangkan hasil penelitian untuk arsitektur Jordan RNN pada parameter 500 hidden neuron , target error 0.0005, learning rate 0.01, dan maksimal epoch 10000 dengan koefisien MFCC 12 menghasilkan akurasi terbaik sebesar 73.55%. Sehingga berdasarkan hasil penelitian yang didapat, arsitektur Jordan RNN memiliki kinerja yang lebih baik dibandingkan dengan arsitektur Elman RNN dalam mengenali ucapan Bahasa Indonesia berjenis continuous speech }, issn = {2777-0648}, pages = {36--44} doi = {10.14710/jmasif.11.2.34874}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/34874} }
Refworks Citation Data :
Article Metrics:
Last update:
Last update: 2025-07-02 15:16:42
The authors who submit the manuscript must understand that the article's copyright belongs to the author(s) if accepted for publication. However, the author(s) grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors should also understand that their article (and any additional files, including data sets, and analysis/computation data) will become publicly available once published under that license. By submitting the manuscript to Jmasif, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. Jmasif will not be held responsible for anything arising because of the writer's internal dispute. Jmasif will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. Jmasif allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and Jmasif to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.
