Judul: Perbandingan Metode Pengenalan Pola Suara Menggunakan Codebook dan Probabilistic Neural Network Berdasarkan Kisaran Usia dan Jenis Kelamin
Abstrak: Voice signal be used to identify a speaker, including the age range and gender based on the difference of its frequency characteristic. This research compares two method of voice identification namely codebook and probability neural network (PNN) in recognizing the age range and gender of the speaker. In this research, the age range is divided into three categories namely children (8-11 years old), teenagers (12-17 years old) and adults (30-50 years old). Each age category is divided based on gender, so that there are six categories in total. This research utilized 600 voice data representing the total six categories. MFCC is used as a method of feature extraction, whereas K-means is used as the clustering method. Several important parameters in the MFCC process are the number of cepstral coefficients, overlap, and time frame. The overlap and time frame values are 0.5 and 40 ms, respectively; whereas the chosen cepstral coefficients to produce the maximum accuracy are 13, 20, and 26. The comparison of voice identification is constructed from three different proportions of training data and testing data (25%:75%, 50%:50%, 75%:25%). It is shown that the accuracy of codebook method is 97.20% whereas that of PNN is 95.17%.
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Judul: Pengembangan model probabilistic neural network (PNN) pada pengenalan kisaran usia dan jenis kelamin berbasis suara
Abstrak: Voice signal can be used to represent a speaker. Voice can also be used to recognize the age range and the gender of the speaker, because of the difference in frequency of the voice signal for different age and gender. Age range for this experiment is divided into children (8-10 years old), teenagers (12-17 years old) and adults (30-50 years old). We performed feature extraction to obtain a representation of the speaker using Mel-Frequency Cepstrum Coefficient (MFCC). MFCC is used with 13, 20, 26 coefficients. Probabilistic Neural Network (PNN) is used as the feature matching model. The PNN model is constructed from three different proportions of TRD (Training data) : TSD (Testing data) (25%:75%, 50%:50%, 75%:25%). MFCC with 26 coefficient gives the best accuracy of 93.47%. The proportion of 75% (TRD) : 25% (TSD) also gives the best accuracy of 94.22%. PNN-age and PNN-gender identification result in an accuracy of 91.26%. Specifically, PNN accuracy percentage for male and female are 92.26%, and 95.20%, respectively, while for children, teenagers and adults the accuracies are 99.85%, 92.28% and 95.57%, respectively. In conclusion, the average of accuracy for all categories is 91.26%. The experiment using the voice of male-teenagers gives the worst accuracy, which is caused by the overlapping frequencies of voice in children, teenagers and adults.
Keyword: Voice identification, PNN model, MFCC, Age-identification, Gender-identification

Judul: Development of Antioxidant Film from Basil Seed (Ocimum citriodourum) Mucilage Incorporated with Thyme Essential Oil.
Abstrak: The objectives of this research was to evaluate the effects of thyme essential oil addition on physical properties and antioxidant activity of lemon basil seed mucilage film. Lemon basil is a common name for the culinary herb (Ocimum citriodourum). This research project investigated the properties of an edible film based on lemon basil seed mucilage (LBSM) incorporated with thyme essential oil (TEO). LBSM (0.4% w/v) films were prepared by adding glycerol as plasticizers at the concentration of 30% w/w based on the weight of LBSM and then incorporated with TEO for antioxidant properties. LBSM films were given different concentrations of TEO (0%, 1%, 2% and 3% v/v) (0% as the control). The result showed that films with 3 % TEO had the highest water vapor permeability (WVP) (3.27±0.38 x10-4g mm/m2d kPa), while the control film showed the lowest WVP (1.36±0.28 x10-4g mm/m2d kPa). Water solubility were decreased with the increase in TEO concentration (p<0.05). 1% TEO films showed the highest tensile strength (5.17±0.54MPa) but lower % elongation at break (11.28±1.86%) compared to the other two films (p<0.05) (excluding the control). Scanning electron microscopy showed that the microstructure of the TEO LBSM films had a critical effect on its physical properties, further supporting the tensile strength and WVP results. The 3% TEO film exhibited the highest radical scavenging activity on DPPH (3.3±0.04 % inhibition) (p<0.05) while the others were very low and had slight difference. Increasing the amount of TEO will increase the antioxidant activity, but further increasing the concentration of TEO will also weaken the physical properties of the film.
Keyword: Lemon basil seed mucilage, edible film, antioxidant, thyme essential oil, film properties