Judul: Perbandingan metode Wavelet Daubechies dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan Probabilistic Neural Network (PNN) sebagai Classifier
Abstrak: Nowadays, the development of telecommunication research is rapidly increasing. One of the research is in sound area. Sound is a way for human to interact with computers, known as word recogniser. Word recogniser is a part of voice recogniser that make the computers possible to receive input from word that pronounced. Word that pronounced contain phonemes that arranged into sentence. Voice recognition technology can recognise and understand words that pronounce by digitalising it, and tuning the digital signal with certain pattern that has been saved in a hardware. The result from this word identification will be displayed into printed word. This research will compare between Wavelet Daubechies and MFCCas identity extraction on word recognition with (PNN) as pattern identifier. PNN is a pattern identifier that has high accuracy. The comparison between trained data and tested data in research is 75% : 25%. Tested data that has been used was vary, such as: testing with increase the noise (pure noise) and data with noise increasing from 30dB, 20dB and 10dB. The result from this research is that the identity extraction by using MFCC is much better than with Wavelet Daubechies. From the pure original data (without noise increasing) the accuracy is 92.3% and in data with noise increasing 30dB, noise 20dB and noise 10dB the accuracy is 50.96%, 26.92% and 19.23%.
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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: Sarcosporidiasis in slaughter animals
Abstrak: Sarcocystis which belong to the generally occuring parasites for they are found almost all over the countries of the world. Infection of Sarcocystis usually throught inges- tion of feces or flesh of infected animals. The pathogenesis of Sarcosporidiasis is considered as benign but when the Sarcocystis die in a host, sarcocystin or sarcosporidin is liberated thereby which lead to condemnation of meat in the abattoir. Besides condemnation of meat, Sarcocystis also causes: loss of appetite, anemia, lameness, weakness in the lion, diarrhea, fever, pyrexia, paralysis, emaciation, neuro logical disturbances, etc. " Amprolium "at the rate of 100 mg/Kg body weight per day has been practiced for prevention or to minimize Sarco- sporidiasis. Treatment must be started during incubation period. Shedding of Sarcocystis sporocysts in the feces of carnivores is the key factor in the spread of Sarcocystis infection. Therefore, control efforts must be based primarily on measures designed to break the cycle of Sarcocystis.
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