Judul: Pengembangan Model Pengenalan Kata Menggunakan MFCC sebagai Ekstraksi Ciri dan PNN sebagai Pengenalan Pola
Abstrak: Utilizing human voice to develop advanced technology is very popular at this modern days; it has many different functions. The objective of this research is to develop a recognition pattern model for words that are usually used by operation system. Eventually it can be utilized to control the operation system by voice. The chosen words to build the recognition model are 20 words: cancel, close, copy, cut, delete, edit, format, insert, maximize, minimize, move, new, open, paste, print refresh, rename, restart, search, and view. In order to get the model for each pattern, 50 times soundwave samples per word were taken. The waveforms are first recorded by computer and saved into the wave file format. The silent part are deleted, standardization are performed followed by feature extraction. The results of feature extraction process are divided into two set of data, the training data and testing data. The ratio of training data to testing data are 75%:25%, 50%:50%, and 25%:75%.The pattern recognition model is built using PNN. Experiment are then performed to measure the performance of the model. For data percentage 75% training data and 25% testing data the general accuracy result is 86%, the result that used 50% training data and 50% testing data is 87%. The last testing result that used 25% training data and 75% testing data is 88%.
Keyword: 

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%.
Keyword: 

Judul: Bioavailabilitas Zat Besi dan Konsumsi Pangan pada Ibu Hamil di Kota Bogor
Abstrak: Tujuan dari penelitian ini adalah untuk memperkirakan bioavailabilitas zat besi pada ibu hamil wanita menggunakan tiga metode berbeda. Penelitian dilakukan di kota Bogor. Desain penelitian ini adalah studi cross-sectional, dan jumlah sampel adalah 203 wanita hamil. Hasil penelitian menunjukkan bahwa anemia prevalensinya adalah 22,2%. Konsumsi makanan hewani sebagai sumber zat besi tadi masih rendah (80 g) mungkin 84,2% sampel memiliki zat besi yang tidak mencukupi. Asupan vitamin C sebagai peningkat bioavailabilitas zat besi sebesar 39,1 mg. Penelitian ini memiliki menunjukkan bahwa asupan zat besi heme (8,9 mg) lebih rendah dibandingkan dengan zat besi non-heme (13,3 mg). Berdasarkan hasil tersebut diperoleh nilai bioavailabilitas zat besi sebesar 3,9 mg. Dia menunjukkan bahwa bioavailabilitas zat besi pada penelitian ini lebih rendah dibandingkan dengan Indonesia RDA. Ketersediaan hayati zat besi dipengaruhi secara signifikan (R2=0,996; p<0,05) berdasarkan asupan dari vitamin A, vitamin C dan zat besi. Korelasi orang menunjukkan tidak ada hubungan (r=0.170; p>0.05) antara bioavailabilitas zat besi dengan status anemia.
Keyword: Iron bioavailability, Food consumption, Pregnant woman