Judul: Pengenalan Suara Berdasarkan Usia dan Jenis Kelamin Menggunakan Algoritme Support Vector Machine (SVM)
Abstrak: Voice recognition using computers is made possible due to the unique characteristics and frequencies that each voice possesses. These vocal characteristics can be grouped according to gender (male or female) or age (child, teenager, or adult). The purpose of this research is to identify individual voices that have been placed within one of the groups based on age and gender. The data used comes from the research of Fransiswa (2010) and consists of six classes: boys (AL), girls (AP), teenage boys (RL), teenage girls (RP), men (TL), and women (TP). The method used to extract vocal characteristics was the Mel Frequency Cepstrum Coefficient (MFCC) and pattern recognition was performed using a Support Vector Machine (SVM) with several variations of the kernel such as Linear, Polynomial, and Radial Basis Function (RBF). The parameters affecting the MFCC process include: The value of the coefficients, overlap, time frame, and the rate of sampling. The average highest accuracy value of 98,24% was obtained using the RBF Kernel
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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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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