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} }
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