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: 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: Penyakit transmissible gastroenteritas oleh virus corona pada babi
Abstrak: Trasnmissible Gastroenteritis pada babi oleh virus merupakan penyakit infeksius yang ditemukan pertama kali oleh Doyle dan Hutchings tahun 1946 di Indiana USA. Sehubungan dengan tingginya mortalitas akibat pe- nyakit TGE pada anak-anak babi serta kerugian yang beru- pa penurunan berat badan secara drastis pada babi yang dewasa, maka perlu kiranya penyakit ini diperhitungkan bagi suatu usaha peternakan. Walaupun di Indonesia be- lum dilaporkan kejadian penyakit tersebut, tetapi kita perlu waspada terhadap kemungkinan masuknya penyakit bersama dengan babi-babi import yang banyak dijumpai di- peternakan besar di Indonesia. ...
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