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
Keyword: 

Judul: Wood Type Identification Using Support Vector Machine Base on Image Data
Abstrak: The wood type identification can be studied as a science, but the ability to identify the type of wood can only be obtained through a long training process, repeatedly and continuously. The problems that appear when workers are not skilled enough to identify the type of wood are the longer time of business process, and thus certainly will added more cost. The wood type identification system is very necessary to provide an alternative solution for this problem. The wood samples had obtained from Balai Pemantauan Pemanfaatan Hutan Produksi Wilayah XII Palangka Raya, which is consists of 4 types of wood, namely: Johar, Jati, Rasamala and Sengon wood. The image of wood type data has obtained using a microscopic camera, and each type has 24 images. Total data in this study are 96 images with has an initial size 480 x 640 pixels and converted into 96 x 128 pixels size. This RGB sample data image is converted into a grayscale images. The image processing in this study using Two - Dimensional Principal Component Analysis (2D-PCA) feature extraction method, which is serves to reduce the images dimensions without removing the important information. The Support Vector Machine (SVM) with kernel function Radial Basis Function (RBF) and polynomial kernel method has been used for classification process. The highest accuracy results which obtained using the RBF kernel at sigma parameter ( ) 35, 40, 45 and 50, within 60% feature extraction are 94.79%. The highest accuracy level which obtained using the polynomial kernel within 1 parameter order and 60% to 75% feature extraction are 95.83%. The results showed that the feature extraction using polynomial kernel produces a higher accuracy than the RBF kernel method.
Keyword: The wood type identification, 2D-PCA, SVM, RBF kernel, the polynomial kernel

Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
Keyword: