Judul: Application of LVQ using K-means initializations for guitar tone chord recognition with MFCC feature extraction
Abstrak: Manusia dapat mengenali suara melalui proses yang dilakukan secara berulang-ulang. Namun, tidak banyak orang yang mampu mengenali suara chord gitar. Penelitian ini bertujuan untuk membuat suatu sistem pengenalan chord gitar menggunakan metode LVQ sebagai pengenalan pola dan MFCC sebagai ekstraksi ciri. Bobot awal yang digunakan pada metode LVQ diperoleh dengan menginisialisasi K-means. Data yang digunakan sebanyak 8640 data suara gitar dengan 24 jumlah chord. Masing-masing data terdiri atas 2 chord yang akan diuji secara terpisah. Parameter LVQ yang digunakan adalah learning rate 0.002, epoh 100, dan penurunan learning rate 0.9. Parameter yang digunakan dalam proses MFCC adalah time frame, overlap, jumlah koefisien cepstral dan jumlah cluster. Hasil percobaan menunjukkan bahwa akurasi maksimum yang diperoleh adalah 83.65% pada time frame 30 ms, overlap 0.4, koefisien cepstral 26, dan jumlah cluster 100., Humans can recognize the sound through a process that is done repeatedly. However, not many people are able to identify the sound of guitar chords. This study used LVQ as pattern recognition and MFCC as feature extraction. Initial weights were initialited by K-means clustering. A collection of 8640 sound data were used with 24 guitar chords. Each data consist of two chords that were tested separately. The parameters used in the process LVQ were 0.002 learning rate, 100 epoch, and 0.9 learning rate decrease. The parameters used in the process of MFCC were the time frame, overlap, the number of cepstral coefficient and the number of clusters. The experimental results showed that the maximum accuracy of 83.65% was obtained with 30 ms time frame, 0.4 overlap, 26 cepstral coefficients, and 100 clusters.
Keyword: chord gitar, Learning Vector Quantization (LVQ), Mel Frequency Cepstral Coefficients (MFCC)

Judul: Pengenalan Chord pada Alat Musik Gitar Menggunakan Codebook dengan Teknik Ekstraksi Ciri MFCC
Abstrak: Human auditory system is capable of extracting rich and meaningful data from complex audio signal. To recognize chord sequences that played in some kind of music is not an easy task. People need big effort to train their sense of hearing so they can recognize that kind sound of chords. This condition is also valid in a computer system. Finding the key and labeling the chords automatically from music are great use for those who want to do harmonic analysis of music. Hence automatic chord recognition has been a topic of interest in the context of Music Information Retrieval (MIR) for several years, and attempts have been made in implementing such systems using well understood signal processing and pattern recognition techniques. This research is about to recognize the sound of chord that played and recorded by guitar instrument. There are 24 major-minor chords that used in this research. MFCC is used as feature extraction and the number of coefficient cepstral that used are 13 and 26. Each chord signal that has been extracted then clustered using K-means algorithm with 8, 12, 16, 20, 24, 28, 32 k numbers to create codebook that use as a model of each chord. For the recognition process, there are two methods that used in this research, unstructured recognition and structured recognition. For the result, this research produces two kinds model of codebook that are codebook with 13 coefficients and codebook with 26 coefficients. Both types of codebook show a good result with accuracy level above 88%. The best result yielded from usage of 26 coefficient cepstral with structured recognition. It’s accuracy level reach 97%. Hence the usage of 26 coefficient cepstral is better than the usage of 13 coefficient cepstral with difference of accuration level is about 7%. This research also shows the affectation of the numbers k-means that used. An increasing accuration level shown by increasing the amount of k-cluster.
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Judul: Pengembangan Bionanokomposit Film Berbasis Tapioka/Nanopartikel Perak dan Tapioka/Nanopartikel Seng Oksida dengan Plasticizer Gliserol.
Abstrak: Cassava starch can be formed into bioplastic film which is transparance as synthetic plastic films. However, pure cassava starch film has some drawbacks such as brittle, poor barrier to water vapour, sensitive to humidity and not stable during the application. The addition of nanoparticles in film was intended to increase the elongation and tensile strength, moisture stability and to provide antimicrobial activity. Meanwhile, the addition of glycerol was intended to increase the plasticity of films so they will not be easily cracked. The main purpose of this research is to elaborate the functional packaging characteristics of bionanocomposites films made from cassava starch with or without silver nanoparticles (Ag-Np) of zinc oxide nanoparticles (ZnO-Np) with glycerol as plasticizer. The addition of nanoparticles was conducted at the amount of 0%, 0,5%, and 1% by weight of the starch, whereas glycerol was used at the amount of 0% and 20% by weight of the solids (starch only or starch+nanoparticles). The value of water vapor transmission decreased significantly with the addition of Ag-Np and ZnO-Np at the concentration of 1% (from 18,0569 g/hm2 to 12,0574 g/hm2) so it enhance the water barrier of the films as packaging materials. The addition of Ag-Np and ZnO-Np at the amount of 1% with 20% of glycerol was also able to inreased the value of elongation (from 1,2% to 88,8%), but decreased the value of tensile strength (from 22,93 MPa to 4,03 MPa). The addition of glycerol gave contrary effect to the tensile strength because it produces low tensile strength values. Moreover, the results show that films made with ZnO-Np had a stronger antimicrobial activity than those made with Ag-Np for both gram positive and gram negative bacteria. In overall, it seems that the formulation of 1% ZnO-Np is the best formulation for developing bionanocomposite film based on cassava starch.
Keyword: Bionanocomposite Films, Silver Nanoparticle (Ag-Np), Zinc Oxide Nanoparticle (ZnO-Np), Cassava Starch, Antibacterial Films