Judul: Pengenalan Chord pada Gitar dengan MFCC Sebagai Metode Ekstraksi Ciri dan Jaringan Saraf Tiruan Sebagai Metode Pengenalan Pola
Abstrak: Human hearing system is capable of identifying sounds or audio signals, especially sounds that are familiar in their daily lives. However, recognizing chord sequences played in some kind of music is not an easy task. People need big effort to train their sense of hearing so that they can recognize chords. This condition is also valid for a computer system. Finding the key and labeling the chords automatically from music are of great use for those who want to do harmonic analysis of music. This research is about to recognize chords played and recorded by a guitar instrument. There are 24 chords used in this research. MFCC was used as a feature extraction using 13 and 26 cepstral coefficients. Each chord signal which has been extracted is modeled using artificial neural networks as a method of pattern recognition. This research results in an accuracy level above 90%. From the research that has been done, it can be concluded that modeling using back propagation neural networks on guitar chords recognition has an accuracy of 92%, better than the codebook method performed in the previous research which resulted in an accuracy of 88%. An increasing accuracy level is shown by using artificial neural networks for pattern recognition
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Judul: Pembangunan Metode Codebook untuk Identifikasi Chord Gitar dengan Teknik Ekstraksi Ciri MFCC
Abstrak: Various types of chords can be produced by a guitar instrument. However, it is difficult to recognize a guitar chord manually. This research develops a system to recognize a chord on the guitar using codebook, MFCC, and K-means clustering. The codebook is used as the pattern recognition method for identification of guitar sound, while MFCC is used as a method of feature extraction. The parameters used in the process of MFCC are the number of cepstral coefficients, overlap, and the time frame, while the parameter used in the process of K-means clustering is the number of cluster. This research used 1440 guitar sound data that are divided into 72 class, so that each class has 20 guitar sound data. The results showed that the maximum accuracy obtained is 99.72% when the number of cepstral coefficients are 26, overlap value is 0.4, time frame is 30 ms, and the number of cluster is 5.
Keyword: Chord, Codebook, Guitar, K-Means, MFCC

Judul: Koksidiosis pada babi yang disebabkan oleh Eimeria debliecki
Abstrak: Eimeria debliecki merupakan penyebab utama yang patogen terhadap timbulnya koksidiosis pada babi, terutama pada babi muda sedangkan babi yang tua umumnya telah mengalami infeksi sehingga hewan menjadi sedikit kebal. Ookista Eimeria debliecki bersporulasi secara lengkap dalam waktu 240 jam (10 hari) pada suhu 20°C. Ookista men jadi lebih infektif terhadap induk semang yang baru. Hewan muda, hewan yang peka bila terinfeksi akan menimbulkan gejala seperti diare, tidak ada nafsu makan (a norexia), dehidratasi dan hewan menjadi kurus. Pada hewan yang sembuh biasanya memperlihatkan pertumbuhan yang terhambat dan hewan menjadi lesu. Kerugian ekonomi yang disebabkan penyakit ini belum dapat ditafsirkan karena belum ada perhitungan kerugian oleh penyakit ini di Indonesia. Diagnosa penyakit dapat dilakukan dengan cara pemeriksaan tinja baik secara langsung maupun dengan dibubuhi zat warna eosin 2% dan pemeriksaan dengan cara/metode konsentrasi. ...
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