Judul: Cascading LBP-GLCM-ANN For Classification Model In Macroscopic Commercial Wood
Abstrak: Manual classification of wood species is time-consuming, consequently, a faster method is needed. One method that can be used is image processing. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods have been widely used for feature extraction but show varying accuracy results. Research is needed to improve the accuracy of image processing in terms of feature extraction. LBP and GLCM are algorithms for feature extraction for images. The classification process used a Backpropagation Artificial Neural Network (ANN BP), which can distinguish several types of wood. This study compared the combined methods of LBP, GLCM, and GLCM for image feature extraction. The best accuracy obtained was 98% for the combined LBP and GLCM methods, while the best accuracy for the GLCM method was 86%. The best JSTPB model for the combined method of LBP and GLCM was parameter 1 hidden layer, 10 hidden layer neurons, learning rate 0.1, and momentum 0.1. The best JSTPB model for the GLCM method was parameter 1 hidden layer, 20 hidden layer neurons, learning rate 0.5, and momentum 0.5. From a total of 80 parameter variations, the accuracy of the LBP and GLCM method was always higher than the GLCM method.
Keyword: Classification, GLCM, JST, LBP, Wood

Judul: Sistem Identifikasi Kayu Ramin Berbasis Citra Menggunakan Local Binary ------- Pattern dan Probabilistic Neural Network
Abstrak: The purpose of this research is to create dekstop application system in effective and efficient way to identify the ramin wood and similar ramin wood based on imagery by using LBPV and PNN methods. LBP variance (LBPV) is used to characterize the local contrast information into the one-dimensional LBP histogram. This research uses the Probabilistic Neural Network (PNN) technique to classify the LBPV. This research was carried out using 21 different types of similar ramin wood and one species of ramin wood. For each type of wood, 20 image were collected. The results of this research conclude that of the 3 operators used one operator was obtained that produce the highest accuracy, which is LPBV (24,3) with 79,77% accuracy. The result of this research indicate that LBPV (24,3) is more accurate to distinguish ramin wood texture and similar ramin wood. This research used only a small-size database, so for further research is needed to use more feature extract methods and types of ramin wood and similar ramin wood
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Judul: Infeksin virus rota pada anak babi
Abstrak: Virus Rota sudah lama diduga sebagai salah satu penyebab diare pada anak babi yang baru dilahirkan. Hal ini diperkuat dengan berhasilnya Mebus dan kawan-kawan mengidentifikasi virus Rota pada kejadian diare yang ter jadi pada anak sapi yang baru dilahirkan. Infeksi virus Rota merupakan hal yang serius, kejadian yang mewabah pernah dilaporkan di Amerika Serikat, Inggris, Kanada dan Australia. Sedangkan kejadian di Indonesia yang dihubungkan dengan infeksi virus Rota belum dilaporkan. Virus Rota termasuk keluarga Reo viridea. Sesuai dengan namanya, virus Rota menyerupai roda (Rota = Wheel), mengandung asam nukleat (nucleid acid) yang berupa asam nukleat ribo (ribo nucleid acid, RNA) dan berserabut ganda (double stranded), tidak mempunyai selubung (envelop atau peplos). Hewan rentan adalah hewan yang berumur sangat muda. Selain menyerang babi, dapat pula menyerang sapi, domba, rusa, anjing, kucing, tikus, kelinci, unggas dan manusia. ...
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