Judul: Interaksi antara Pestisida dan Infeksi Bakteri Aeromonas hydrophila pada Ikan Mas (Cyprinus carpio L).
Abstrak: lkan mas merupakan salah sat ikan konsumsi air tawar yang telah banyaku dibudidayakan di Indonesia. DaJam kegiatan budidaya ini, diperlukan suplai air yang cukup besar dan sumber utamanya berasal dari perairan umum. Namun, buangan limbah rumah tangga. iodustri dan pertanian mengakibatkan tercemarnya perairan. Salah satu bahan pencemar yang berasal dari kegiatan pertanian adalah pestisida. Pestisida jenis insektisida golongan piretroid sintetis telah banyak digWlakan untuk membunuh hama pengganggu tanaman. Keberadaan pestisida dalam perairan dikhawatirkan dapat menyebabkan ikan mengalami stres, pada kondisi ini daya tahan tubulnya akan menurun dan ikan akan mudah terinfeksi patogen.
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Judul: Aplikasi Probiotik, Prebiotik dan Sinbiotik Melalui Pakan untuk Pencegahan Infeksi Aeromonas hydrophila pada Ikan Mas Cyprinus carpio
Abstrak: Penyakit motile aeromonad septicemia (MAS) merupakan kendala yang sering dialami dalam budidaya ikan mas. Salah satu alternatif yang dapat dilakukan untuk mengatasi kendala tersebut yaitu dengan aplikasi probiotik, prebiotik dan sinbiotik. Penelitian ini bertujuan untuk menguji efektivitas pemberian bakteri probiotik, prebiotik dan sinbiotik terhadap kelangsungan hidup dan pertumbuhan ikan mas yang diinfeksi dengan A. hydrophila. Sebanyak 15 ekor ikan mas dengan bobot rata-rata 5,09±0,09 g dipelihara di akuarium dengan volume 96 L. Perlakuan yang diberikan yaitu kontrol negatif (K-), kontrol positif (K+) tanpa penambahan probiotik, prebiotik dan sinbiotik, perlakuan penambahan 1% probiotik (A), perlakuan penambahan 2% prebiotik (B) dan perlakuan penambahan sinbiotik (C; 1% probiotik dan 2% prebiotik). Setelah 30 hari, semua perlakuan (kecuali K-) diuji tantang dengan menyuntikkan A. hydrophila sebanyak 100 μL/ekor pada konsentrasi 107 CFU/ml secara intramuskular dan diamati mortalitasnya selama 14 hari. Hasil penelitian menunjukkan bahwa penambahan sinbiotik memberikan kelangsungan hidup yang lebih tinggi (100%) dibandingkan dengan perlakuan kontrol positif (45,83%) serta memberikan pengaruh yang lebih baik terhadap pertumbuhan ikan mas.
Keyword: Aeromonas hydrophila, ikan mas, probotik, prebiotik, sinbiotik

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