Judul: Identifikasi campuran nada pada suara piano menggunakan codebook
Abstrak: It has been identified blend of tones, either as a single tone or a mix tones using the mel-frequency cepstrum coefficients (MFCC) as feature extraction and modeling of codebook for pattern recognition. The voices which used are the sound of piano and identified as 12 single-tones and 66 mix of tones recorded with 11 kHz on the duration of 1 second. The making of codebook is applied step by step, that is the codebook with the blend and the codebook of tones (single and mixed tones) which was developed using clustering techniques. The results of research showed that the optimum number of codewords is 20 with width of frames is 256 data, with an accuracy is 98.2%. However, there are some tones which are difficult to be identified, they are CC#, CD, CF, and A#B which has accuracy of each below 50%. For C# tone is more often recognized as C#D# tone, for the CC# tone is more often identified as C tone, for CD tone is more often recognized as C# tone, the CF tone is more often identified as C# tone and CF# tone, while for the A#B tone is more often identified with A# tone. Some errors happen in this recognition because those tones have similiar signal pattern so that they have the vector points which adjacent to each other.
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Judul: Penerapan Model Codebook untuk Transkripsi Suara ke Teks dengan Ekstraksi Ciri Mel-Frequency Cepstrum Coefficients (MFCC)
Abstrak: The advancement of information technology has triggered various demands in utilizing computer. One of them is to make computers able to communicate naturally with humans. This study uses MFCC as a feature extraction method and codebook as a pattern recognition method for voice-to-text transcription. The clustering technique used in this study is k-means. Data obtained from MFCC are clustered using the k-means method, and the model for classification is constructed using the codebook method. The utilized MFCC parameters are sampling frequency 11 000 Hz, time frame 23.27 ms, overlapping 39%. 300 voice data in WAV files with 5 seconds of duration each, are used as the training data and test data to determine the number of cepstral coefficients and the number of cluster that can produce the highest accurancy. The experiment is conducted by recognizing each syllable in 60 the test data with 240 training data. Simulation result shows that the maximum accurancy obtained is 98.3% at 26 cepstral coefficients and 12 clusters.
Keyword: voice transcription, MFCC, k-means, codebook

Judul: Pengaruh vaksinasi terhadap dinamika populasi pada model SIR (Susceptible-Infected-Recovered)
Abstrak: SIR model can be used to model the spread of diseases in a closed population based on certain assumptions. In this research, the SIR model is formulated by including the factor of vaccination. As the result, the model has been shown to have two equilibrium points, i.e. disease free and endemic equilibrium. A simulation study is carried out to illustrate the model. The results show that some equilibrium points are asymptotically stable. Moreover, a minimum level of vaccination has been defined. For the vaccination level less than the minimum level, increase in the vaccination level results in decrease of the proportion of infected individuals in the corresponding equilibrium point. On the other hand, for the vaccination level greater than the minimum level, increase in the vaccination level results in decrease the proportion of susceptible individuals in the corresponding equilibrium point. Furthermore, when vaccination level is greater than the minimum level, it is found that the time needed for the disease to extinct is shorter.
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