Judul: Pengenalan tanda tangan menggunakan algoritme VFI5 dengan citra pelatihan tunggal
Abstrak: Biometrics is the science of establishing the identity of an individual based on the physical, chemical or behavioral attributes of the person. Today, one of the most widely applied biometric object is hand-written signature. In this research, we tried to identify scanned offline hand-written signature using VFI5 algorithm. The classification method is only using a single image as a training image. The feature used as the input for VFI5 algorithm is the gray level intensity of the image and the signature image dimension used in this research is 60 × 40 pixels which means originally there are 2400 features to compute. Then, we reduce the dimension using imresize function with nearestneighbor interpolation method in Matlab. The reduced image dimensions are 45 x 30, 30 x 20, 15 x 10, and 7 x 5 pixels. The reduced images then classified using VFI5 algorithm, with 84.33%, 78.44%, 59.11%, and 32.89% accuracy respectively for the first, second, third, and fourth reduced dimension. The result of this research also shows that the accuracy of the recognition is influenced by the image dimension. It turned out that the smaller the dimension of the image, the lower the accuracy of the recognition. Smaller image dimension also resulted in less training and testing time.
Keyword: Signature recognition, VFI5 algorithm, Single training image, Imresize, Nearest-neighbor, Bogor Agricultural University

Judul: Signature Recognition using VFI5 Algorithm and Wavelet Preprocess.
Abstrak: Biometric is a science in recognizing the identity of a person based on their physical or behavioral traits. Today, one of the most widely applied biometric object is hand-written signature. In this research, we tried to identify offline handwritten signature using VFI5 (Voting Feature Intervals 5) classification algorithm. The VFI5 classification algorithm has been used previously on non-image objects with promising result. The feature used in the classification is the grayscale value of the image and the signature image dimension used in this research is 40×60 pixels which means originally there are 2400 features that relatively large to compute. Thus, Haar wavelet transform is used to reduce the dimension. This is done by taking only the approximation image of the transformation result, and leave the horizontal, vertical, diagonal details. The wavelet decomposition is applied until the fifth level, creating new low level images with 20×30, 10×15, 5×8, 3×4, 2×2 dimension respectively for the first, second, third, fourth, and fifth decomposition levels. The approximation images then classified using VFI5 algorithm, with 97.5%, 95%, 90%, 65%, and 62.5% accuracy respectively for the first, second, third, fourth, and fifth decomposition levels. It turned out that VFI5 algorithm can be used to identify images with wavelet transform to reduce the image dimension with good result, and the higher decomposition level applied, the more accuracy dropped. Keywords: signature identification, wavelet image dimension reduction, image classification, VFI5 algorithm.
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Judul: Koksidiosis pada Sapi Disebabkan Eimeria Zuernii (Rivolta,1887)
Abstrak: Koksidia merupakan parasit intraseluler yang menyerang traktus digestiyus terutama bagian USUS. Penyebab penyakit ini adalah dari genus Eimeria. Koksidia yang paling patogen terhadap ternak sapi adalah Eimeria Ziuernii (Rivolta, 1887 dalam Todd dan Ernst, 1977). Siklus hidup Eimeria zuernii terdiri dari fase eksogen yang disebut juga dengan tahap sporogoni dan fase endogen yang terdiri dari tahap skizogoni dan tahap gametogoni (Soulsby, 1982).
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