Judul: Toksisitas Niklosamida Terhadap Pertumbuhan, Kondisi  Hematologi dan Histopatologi Juvenil Ikan Mas (Cyprinus  carpio)
Abstrak: Penggunaan pestisida dalam bidang pertanian (sawah) yang semakin meningkat telah menimbulkan dampak negatif bagi menurunnya kualitas lingkungan perairan, karena pada umumnya sebagai tempat pembuangan limbah cair pertanian yang masih mengandung residu pestisida. Akibat kegiatan tersebut maka lingkungan perairan tawar yang merupakan sumber air untuk kegiatan budidaya perikanan berpotensi tercemar oleh berbagai bahan aktif yang terkandung dalam formulasi pestisida.
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Judul: Toksisitas akut dan respon tingkah laku juvenil ikan mas Cyprinus carpio terhadap moluskisida niklosamida
Abstrak: Directorate of Food Crop Protection noted that in 2007 approximately 22 110 ha of rice field pests of mulberry snails (Pomacea canaliculata) and to regions of West Java, there are 6829 hectares affected. Molluscicides containing the active ingredient of niclosamide is a pesticide used by farmers to eradicate the snail. Previously, the active ingredient in the year 1953 is used to eradicate fish lamprey (Petromyzon marinus), which is a parasite for commercial fish in the Great Lake (United States). While in use, that molluscicides have the potential for environmental damage in the surrounding waters used for aquaculture activities is one example is the cultivation of common carp (Cyprinus carpio) and toxicity studies of niclosamide in Indonesian has not yet been done. Therefore, research must be done to determine the acute toxicity of niklosamida on carp and carp find out the response time in direct contact with that poison. Result from this research, the acute toxicity from niclosamide toward juvenile carp (Cyprinus carpio) including at the level A which the power of toxicity is very high because value LC50-96 hour are smaller than 1 mg/L, that is 0.08 mg/L. Initial response of juvenile common carp when exposed the poison of niclosamide are occurrence of fish mucus secretion in the two hour at all concentrations (0.14, 0.28, 0.39, 0.54, dan 0.75 mg/L). Fish solitude which sometimes lean on the corner of the aquarium's going on hour four in the concentration of 0.39 mg/L but at a higher concentration of 0.54 mg/L and 0.75 mg/L fish swam out of balance or weak and go down to the bottom of the aquarium, sometimes with inverted body. Fish prefer to swim on the surface to breathe air that occurred on the twenty-four hour at the concentration of 0.54 mg / L. While the respon of sequel are fish swam out of balance or weak and go down to the bottom of the aquarium which sometimes happens with the body upside down in the thirty-six hours at concentrations of 0.2 mg/L, 0.54 mg/L and 0.75 mg/L. Change of the skin color becomes pale happens on the sixty hours at concentration 0.39 mg/L, 0.54 mg/L, and 0.75 mg/L.
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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