Judul: Pengembangan Model Pengenalan Wajah pada Ruang Eigen dengan Jarak Euclid dengan 2DPCA
Abstrak: Face recognition is an issue that is based on object recognition. Objects identified with specific and measurable characteristics. In the process of face recognition methods quite a lot that can be used, one using the Euclidean distance function method. Euclid's method is the method by comparing the image of testing with the minimum distance to the image database training. Feature extraction techniques used in face recognition to find important features of the training image database that will be a reference. One is a technique introduced by Yang and Zhang in 2004 was 2DPCA (2 Dimensional Principal Component Analysis). Our experiments with used 2DPCA for feature extraction techniques at face recognition. The results achieved that the performance of the model with a percentage correct of 98.75%
Keyword: 

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation

Judul: Anemia akibat defisiensi besi pada ternak
Abstrak: Cairan tubuh yang mudah diperoleh adalah darah. Darah mempunyai berbagai macam fungsi, dan diantaranya yang penting dalam menjalankan fungsi darah adalah hemoglobin. Besi merupakan unsur yang memainkan peranan pada proses kehidupan dan merupakan unsur pokok dari pigmen hemoglobin. Berat molekulnya 68.000 pada semua spesies hewan dan rata-rata mengandung 0,34% besi. Besi juga merupakan komponen dari beberapa enzim termasuk sitokrom dan flavo- protein. Jika fisiologis darah terganggu, antara lain karena bakteri, parasit, zat kimia, kurang gizi ataupun oleh sebab lainnya, dapat menyebabkan penurunan jumlah sel-sel darah merah yang beredar, juga menyebabkan penurunan ka- dar hemoglobin didalam darah atau penurunan nilai hema- tokrit dibanding dengan keadaan normal. Dalam keadaan demikian akan mengakibatkan timbulnya anemia. Kekurangan gizi khususnya besi akan menimbulkan anemia yang disebut anemia gizi besi. Penurunan kecepatan produksi, kehilangan atau destruksi sel darah merah yang meningkat dapat diakibatkan oleh perdarahan akut atau kronis, atau dapat ditimbulkan oleh faktor toksik (racun atau infeksi) yang menyebabkan hemolisis dan peningkatan destruksi eritrosit. Kegagalan pembentukan eritrosit juga dapat disebabkan oleh kekurangan besi dan protein dalam makanan. Gejala yang ditimbulkan karena anemia tergantung pada penyebabnya. Namun gejala yang menyolok dari defisi- ensi besi, karakteristik kliniknya adalah pertumbuhan yang kurang baik, kelesuan, kepucatan membrana mukosa, penambahan laju respirasi dan pengurangan kekebalan ter- hadap infeksi, Weinberg, 1974 (Underwood, 1981). Akibat terjadinya anemia tidak hanya mempunyai dampak negatif yang langsung diderita oleh ternak, tetapi juga akan dirasakan peternak. Akibat tersebut antara lain kesehatan ternak terganggu atau ternak merasa sakit atau pertumbuhan kurang baik, penurunan atau hilangnya kemampuan kerja pada ternak akibat kelemahan. Juga kekurusan dan ikterus yang dapat menyebabkan daging bisa diafkir sehingga tidak dapat dijual atau dimakan…dst
Keyword: