Judul: Perbandingan metode voting feature intervals dengan jaringan saraf tiruan dalam mengklasifikasi genre musik
Abstrak: Music genre is one of the important descriptions that have been used to classify digital music. The aim of this research is to compare Voting Feature Intervals (VFI) methode with the Neural Network (NN) methode in classifying music genre. There are 12 scenarios of feature extractions in this research. Three variations of MFCC coefficient number (7, 13 and 20 coefficients) and four variations of music length (1, 5, 10, and 30 seconds). From each of the feature vector, mean was calculated. For the NN methode after the feature vectors were extracted, normalization was applied using the cumulative normal distribution methode. This research shown that the optimal number of MFCC coefficients was 13 coefficients. NN predictions were better than VFI predictions. NN has an accuracy up to 95% which was obtained by using 30 neurons of hidden layer, 10 seconds length of music and 13 MFCC coefficients. While the VFI has an accuracy up to 85% which was obtained by using 30 seconds length of music and 7 MFCC coefficients. Both experiments that used 13 and 20 coefficients of MFCC feature obtained same accuracy using the NN method. Classic genre has an accuracy of 100% in VFI. The reliability of the system was 57,14% for disco up to 94,44% for classic.
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Judul: Pengklasifikasian Genre Musik Berdasarkan Sinyal Audio Menggunakan Support Vector Machine
Abstrak: Musical genre is a label for musical art to characterize and categorize music. The determination of musical genre is done based on the similarity between the music. Most of the classification of musical genre is done manually, which requires a lot of effort and time especially when there is a big database of music. The purpose of this research is to build a support vector machine model to automatically classify the musical genre and implement the method to extract the musical surface and rhythm features. The developed model is then utilized to determine the genre from the unknown music. Support vector machine is a learning system whose classification uses a hypothesis space in the form of linear functions in a high dimension of feature space. The extracted musical surface and rhythm features are the centroid, roll off, flux, zero crossings, low energy, and four peaks of the signal‟s autocorrelation calculation. The result of this research is a classification model with an average accuracy of 65%.
Keyword: support vector machine, musical genre, audio signal

Judul: Enteric Collibacillosis Pada Anak Babi Baru Lahir: Patogenesa dan Perubahan-Perubahan Patologisnya
Abstrak: Diare disebabkan oleh infeksi bakteri Escherichia coli pada anak-anak babi baru lahir yang peka terhadap en terotoksin produk bakteri tersebut. Dikenal sebagai colibacillosis. Patogenesa dari colibacillosis anak babi baru lahir adalah (1) infeksi oleh E. coli patogen, (2) proliferasi E. coli di lumen usus halus, (3) produksi dan pelepasan enterotoksin, dan (4) aktifitas toksin pada usus halus. Dalam proses proliferasi, adanya antigen pili K88 sangat penting karena memungkinkan bakteri perlekatan dengan epitel mucosa usus halus. mengadakan Setelah ko lonisasi dan proliferasi segera toksin dilepaskan. Akti- fitas toksin menginduksi sekresi yang berlebihan dari u- sus sehingga banyak cairan yang dikeluarkan ke lumen usus. Hal ini mengakibatkan banyak cairan dan elektrolit hilang sehingga timbul diare, dehidrasi dan asidosis yang sering menyebabkan kematian. Perubahan-perubahan patologis yang umum adalah dis tensi usus halus, dilatasi lambung dan infark pada kurva- tura mayor. Perubahan-perubahan histologis yang terjadi adalah enteritis hemorrhagica acuta, oedem dan kongesti pembuluh darah di lamina propria, akumulassi leukosit inti polimorf, sel monosit, dan inti sel mengalami piknosis Thrombus pada vena dan degenerasi serta desquamasi sel epitel usus….
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