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: Speaker Identification System Modeling Using MFCC as Feature Extraction and SVM as Pattern Recognition
Abstrak: Everyday people exchange information using voice may also exchange information with the media texts and tools. Voice signal every human has the character and qualities of different or unique. Indonesia has a diverse ethnic, communities and cultures, many problems are for the same word has different pronunciation patterns. Because of this problem could be a problem in the speaker identification system, so it is necessary to develop a system that is relatively more robust to the problem of intra-speaker variability and noise. Speaker identification system is more focused on the analysis of the two subsystems, namely Feature Extractor and Pattern Recogniser. Mel-Frequency Cepstrum Coefficients (MFCC) is one of feature extraction that is often used for processing the human voice for calculating the cepstral coefficients with the consideration of human hearing. Support Vector Machine (SVM) is one of the classification techniques of data with the supervised learning that is able to classify the multi-class so it is suitable for the classification of more than two classes. Data collection was performed using a microphone to record sound. Sound source was obtained from 10 adult speakers with differences in gender, age and ethnicity, which each speakers say 50 times the word "COMPUTER" so that obtained 500 data. Record duration is 2 seconds with a frequency of 16 KHz. Before data is processed, a preprocessing stage consisting of the elimination of silence, normalization and noise addition. Gaussian noise is added from the level of 80 dB to 0 dB. After the MFCC feature extraction is done, the next stage is SVM pattern recognition using QP and SMO algorithms. Kernel function tested for RBF, Linear, and Quadratic for each algorithm. Pattern Recognition using Kernel quadratic function with a ratio of 90 : 10 for the test data that the original sound without noise, SMO algorithm produces accuracy of 97.0% and the accuracy of the system can maintain above 70% up to 40dB noise addition. The number of errors for all 10 speakers using the test data of the original sound without noise is at most the number 9 speakers (Male, 41 years old, Java). The processing time SMO algorithm is better than the QP algorithm. Future studies may be added to increase the accuracy of Noise Cancelling the voice data is contaminated by noise.
Keyword: Speaker Identification, MFCC, SVM

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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