Judul: Tingkat kelangsungan hidup anak ikan nila (Oreochromis niloticus) yang ditransportasikan secara tertutup pada kondisi jalan yang berbeda
Abstrak: Permintaan terhadap ikan bertambah sejalan dengan meningkatnya kesadaran terhadap kesehatan di masyarakat. Bahkan pada saat ini, ikan yang dikonsumsi tidak hanya ikan yang berukuran sedang dan besar, tapi juga ikan yang berukuran kecil atau anak ikan. Karena ukurannya yang sangat kecil, maka perlu dilakukan penanganan yang berbeda dari ikan-ikan yang berukuran lebih besar. Salah satu aspek yang perlu diperhatikan dalam penanganan ikan adalah transportasi. Trasportasi merupakan langkah akhir dari proses produksi. Kebanyakan kehilangan ikan karena kematian maupun cedera pada tahap ini, sangat mempengaruhi keuntungan (Jensen 1990).
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Judul: Kajian penggunaan berbagai bahan aditif pada media transportasi tertutup terhadap kelulushidupan benih ikan nila Oreochromis niloticus
Abstrak: Ikan Nila merupakan salah satu komoditas yang memiliki tingkat pembudidayaan yang cukup tinggi di sektor ikan air tawar di Indonesia sehingga memerlukan penanganan transportasi dengan baik untuk penyaluran benihnya. Sistem transportasi yang digunakan pada penelitian yaitu transportasi benih ikan nila sistem tertutup. Penggunaan bahan aditif pada media transportasi dilakukan dengan tujuan mempertahankan kondisi lingkungan media serta melakukan penurunan metabolisme ikan selama pengangkutan berlangsung. Penelitian dilakukan dengan metode studi pustaka berhubungan dengan penambahan bahan aditif pada transportasi benih ikan nila sistem tertutup. Bahan aditif yang dianalisis di antaranya bahan anestesi (ekstrak akar tuba, ekstrak bunga kamboja, dan ekstrak daun bandotan) dan sebagai stabilizer kualitas air pada media transportasi berupa zeolit, karbon aktif dan garam. Penggunaan garam, zeolit, dan karbon aktif sebagai stabilizer kualitas air dapat digunakan pada sistem transportasi yang membutuhkan waktu yang lebih dari 10 jam sedangkan untuk waktu dibawah atau sama dengan 10 jam maka dapat menggunakan ekstrak daun bandotan sebagai bahan anestesi untuk menurunkan laju metabolisme pada ikan., Tilapia is a commodity that has a fairly high level of aquaculture in the freshwater fish sector in Indonesia, so it requires proper transportation handling for the distribution of seeds. The transportation system used in this study is the closed system of tilapia seed transportation. The use of additives in the transportation media is carried out with the aim of maintaining the environmental conditions of the media and reducing fish metabolism during transportation. The research was conducted using the literature study method related to the addition of additives in closed system tilapia seed transportation. The additives analysis included anesthetic agents (tuba root extract, frangipani flower extract, and bandotan leaf extract) and as stabilizers for air quality in transport media in the form of zeolite, activated carbon and salt. The use of salt, zeolite, and activated carbon as air quality stabilizers can be used in transportation systems that require more than 10 hours while for times below or equal to 10 hours, bandotan leaf extract can be used as an anesthetic agent to reduce the metabolic rate in fish.
Keyword: additives, tilapia, survival rate, transportation

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