Judul: Pengenalan Tanda Tangan dengan Citra Pelatihan Tunggal Menggunakan Algoritme VFI5 Berbasis Histogram
Abstrak: Biometrics is the science of establishing the identity of an individual based on the physical, chemical or behavioral attributes of the person. Signature is one of the most common object used in biometric system. In this research, we will use VFI5 algorithm to classify offline signature images. The classification method in this research 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 which represented as a histogram. Then, the histogram is divided into six types of interval. But before dividing the histogram, the images are partitioned into a few parts with fifteen different sizes. The result of this research indicates that images partitioned horizontally have better accuracy than images partitioned vertically. The highest accuracy in this research is 95.56% which is obtained using 15 x 10 pixels image partition. The result of this research also shows that the accuracy of the recognition is influenced by the size and the number of partition.
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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: 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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