Judul: Pemodelan Support Vector Machhine Untuk Identifikasi Jenis Tanaman Paku Berdasarkan Citra Spora Dalam Ruang Eigen
Abstrak: Spores of is a breeding tool of ferns plant (Pteridophyta), which generally exist beneath the surface of each leaf. The research of Pteridophyta spores characteristic which commonly done is observing the size and shape to see what kind of spores. The method requires a lot of comprehension, experience, accuracy and time to achieve high accuracy in the determination of the type. Based on this reason, it is necessary to develop another technique by modeling to help identify the type Pteridophyta on the spore part. The purpose of this study is to make Support Vector Machine (SVM) modeling to identify the types of ferns by spore image in the eigen space. The data used was the image of the spores with four classes, each has 24 data per class. The method used in this study was the 2D-PCA for feature extraction and SVM for classification of data by using RBF and polynomial kernel functions. The result of this study is the accuracy of any extraction that has been attempted. The best result lies in the RBF kernel function with feature extraction of 70% and parameter 10 with an accuracy of 98.96%.
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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: Konsumsi Pangan dan Status Gizi Ibu Hamil di Kecamatan Danurejan, Kodya Yogyakarta, Daerah Istimewa Yogyakarta
Abstrak: Hasil penelitian menunjukkan bahwa rata-rata konsumsi energi per orang per hari adalah 1 525 Kalori, konsumsi protein 45,l g, konsumsi zat besi 12,8 mg dan konsumsi vitamin A 2 264 SI. Rata-rata kecukupan energi per orang per hari adalah 2 213 Kalori, kecukupan protein 75,6 g, kecukupan zat besi 29,O mg dan kecukupan vitamin A 4 500 SI.
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