Judul: Pengembangan Model Probabilistic Neural Network untuk Identifikasi Chord Gitar
Abstrak: Pengenalan kunci gitar membutuhkan teknik ekstraksi ciri dan model pengenalan pola. Penelitian ini membangun sistem pengenalan suara gitar menggunakan teknik MFCC sebagai pengekstraksi ciri dan PNN sebagai pengenal pola. Tujuan penelitian ini adalah menemukan parameter-parameter yang menghasilkan akurasi terbaik. Parameter-parameter tersebut terkait dengan MFCC, yaitu jumlah koefisien cepstral, overlap, dan time frame. Sistem bekerja dengan membaca file suara berformat WAV, kemudian file diproses dengan teknik MFCC dan PNN untuk menghasikan keluaran kunci suara gitar berupa teks. Penelitian ini menggunakan 8 280 data suara gitar dari 24 chord yang berbeda. Hasil penelitian menunjukan bahwa koefisien cepstral 52, overlap 0.4, dan time frame 100 ms menghasilkan akurasi maksimum yaitu 96.56%.
Keyword: identifikasi kunci gitar, mel frequency cepstral coefficients (MFCC), pengenalan suara, probabilistic neural network

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: The Effect Cassava Leaves Meal in The Diets on Body Weigbt, Percentage of Digestive Tract and Viscera of Mule Duck.
Abstrak: The mule ducks are the crossbreed between common duck females (Anas plathyrhynchos) and muscovy ducks (Cairino moschata) to improve the meat production in order to meet the animal protein requirement of the people. One of the problems in husbandry farming is highly feed cost. This problem can be solved with using highly available local sources and cheapness. Cassava leaves (Manihot esculenta Crantz) is the one local sources which can be used as an alternative feed, because it contains a rather high protein (24.1%) aud vitamin A (10,000-13,000 IU). A highly Hydrocyanide (HCN) as well as crude fiber in the cassava leaves are the limiting factors to be used as poultry diet. The ducks have more ability in digest the crude fiber than chicken. So that, using some cassava leaves in the diet does not make any problem.
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