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 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: Perencanan Lanskap Sunda Kalapa, Jakarta Utara Sebagai Kawasan W isata Sejarah
Abstrak: Perkembangan kota Jakarta dari waktu ke waktu sangatlah cepat, sehingga menyebabkan kota-kota tua banyak yang tergusur untuk dijadikan daerah baru yang lebih modern. Apabila hal tersebut tidak ditanggulangi secara dini maka keberadaan Kota Lama Jakarta akan hilang. Hal inilah yang melatar belakang untuk dilakukan tindakan perlindungan dan revitalisasi kawasan bersejarah Sunda Kalapa.
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