Judul: Pemanfaatan zeolit dan karbon aktif pada sistem pengepakan tertutup ikan corydoras Corydoras aenus dengan kepadatan tinggi
Abstrak: Budidaya ikan hias sangat berkembang pesat di Indonesia. Oleh karena itu, Indonesia merupakan salah satu negara pengekspor ikan hias utama di dunia Internasional. Salah satu ikan hias yang sedang mengalami peningkatan permintaan di tahun 2007 ini adalah ikan Corydoras.
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Judul: Produksi Ikan Corydoras Corydoras aenus pada Padat Penebaran 8, 12 dan 16 Ekor/liter dalam Sistem Resirkulasi.
Abstrak: Salah satu ikan hias yang berprospek untuk dikembangkan adalah ikan corydoras (Corydoras aeneus). Permintaan ikan corydoras dari beberapa negara Eropa dan Asia cukup tinggi yaitu sebanyak 10.000 – 20.000 ekor/bulan, akan tetapi permintaan tersebut belum mampu dipenuhi produksi dalam negeri (Marliani, 2007). Hal ini disebabkan oleh rendahnya produktivitas para pembudidaya yang masih menggunakan sistem tradisional dengan padat penebaran yang rendah, yaitu kurang dari 3 ekor/liter yang dipelihara pada bak-bak semen di luar ruangan. Upaya intensifikasi budidaya ikan corydoras dapat dilakukan dengan meningkatkan padat penebaran dan pengelolaan lingkungan perairan yang baik dengan sistem resirkulasi. Kepadatan yang optimal dapat memaksimalkan produksi. Penentuan kepadatan yang optimal merupakan langkah awal yang penting pada kegiatan budidaya.
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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