Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
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Judul: Perbandingan pemodelan Wavelet dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan teknik jaringan syaraf tiruan sebagai classifier
Abstrak: Researches on voice signals have been carried out using various signal processing methods, such as Linear Prediction Coding (LPC), Mel Frequency cepstrum coefficients (MFCC), and Neural Predictive Coding (NPC) which the whole method is based on Fourier transformation. Therefore, comparisons will be made with other approaches based on Wavelet transformation. This research would be the comparison of two feature extraction types, that are Wavelet daubechies and MFCC. Wavelet Transformation has become increasingly popular in signal processing as image and speech. Wavelet transformation has demonstrated good time-frequency localization properties and are appropiate tools for the analysis of non-stationary signals like speech. MFCC feature extraction that computes the cepstral coefficients by considering the human hearing. ANN multilayer perceptron, known as backpropagation used as classifier. From the research that has been done, it can be concluded that the use of Daubechies Wavelet methods as feature extraction on phoneme recognition is not better than MFCC. The accuracy of Daubechies Wavelet method with a 220 hidden neurons ANN learning has achived 36% rate as the best test results. While on phoneme recognition with MFCC method achieved 100% accuracy rate with a 240 hidden neuron learning ANN.
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Judul: Umur dan Pertumbuhan Ikan "Shirogisu" Sillago japonica di Teluk Omura, Nagasaki, Jepang
Abstrak: Ikan Sillago japonica merupakan jenis ikan Sillago yang paling banyak terdapat di Jepang. Ikan ini merupakan jenis ikan konsumsi yang cukup mahal jika diolah sebagai "tempura" dan "shashimi", selain itu juga populer untuk olah raga pemancingan. Oleh karena itu diperlukan usaha yang tepat untuk mengelola perikanan tersebut yang didasarkan pada informasi biologis salah satunya adalah pertumbuhan.
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