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%.
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Judul: Model Fonem dengan Pendekatan Distribusi Normal untuk Pengenalan Kata Menggunakan MFCC sebagai Ekstraksi Ciri
Abstrak: Voice recognition is a field study in voice processing. Research on voice signal has several methods of processing, one of them is the Mel Frequency Cepstrum Coefficients (MFCC). The purpose of MFCC is to extract voice features. The goal of this research is to apply MFCC as a feature extraction and the normal distribution as a method for word recognition. The first step of the research is data reprocessing, and then data extraction using MFCC. Afterwards, the normal distribution method is used to process the data. From the result, it can be concluded that the normal distribution method can be used for word recognition.The results obtained from the word recognition using normal distribution and MFCC as feature extraction have the highest accuracy of 100% for the phonemes /g/, /n/, /p/, /v/, /x/, /y/ and the lowest accuracy of 67% for the phonemes /a/ and /k
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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