Judul: Pengaruh padat penebaran ikan nila untuk pengendalian pertumbuhan gulma air
Abstrak: Penelitian dilakukan mulai tanggal 25 Maret 1982 sampai dengan 6 Mei 1982, di Fakultas Perikanan Institut Pertanian Bogor; untuk mengetahui pengaruh padat penebaran ikan nila dalam mengendalikan pertumbuhan gulma air. Ikan uji yang digunakan dalam penelitian ini adalah ikan nila yang berukuran 8-10 cm, sedangkan gulma air uji yang dipakai adalah eceng gondok (Eichhornia crassipes (Mart.) Solms.), ganggeng (Hydrilla verticillata L.f Royle) dan kiyambang (Salvinia molesta D.S. Mitchell). Wadah yang digunakan adalah bak-bak teraso yang bervolume 0,10 m³. Hasil penelitian menunjukkan bahwa ikan nila dengan padat penebaran 2, 4, dan 6 ekor/bak ternyata mampu mengendalikan pertumbuhan gulma air eceng gondok, ganggeng dan kiyambang. Ikan nila dapat merusak akar eceng gondok dan kiyambang serta seluruh bagian tubuh ganggeng. Berat badan ikan nila pada akhir penelitian ternyata tidak berbeda besar dengan berat pada awal penelitian. Sedangkan kualitas air dalam bak selama penelitian memenuhi syarat untuk kehidupan ikan nila dan ketiga jenis gulma air yang ada.
Keyword: 

Judul: Pengaruh padat penebaran terhadap pertumbuhanmortalitas, dan produksi ikan nila pada kantong jaring terapung di danau lido ci gombong kabupaten bogor
Abstrak: Suatu penelitian tentang padat penebaran telah dilakukan selama 3 bulan di Danau Lido Cigombong, Kabupaten Bogor. Tujuan penelitian ini adalah untuk mendapatkan data tentang pengaruh padat penebaran terhadap pertumbuhan, mortalitas, dan produksi ikan nila yang dipelihara dalam kantong jaring terapung dan diberi makanan buatan pellet. Petak-petak penelitian terletak pada rakit 10 x 10 m. Rakit terbagi dalam 9 petak masing-masing berukuran 3x3m², tersusun dalam formasi latin square 32. Pada setiap petak dipasang sebuah kantong jaring berukuran 3 x 3 x 2m³, terbuat dari benang polyethylene 380 D/15 bermata jaring 2,54 cm. Ikan uji yang digunakan adalah ikan nila yang mempunyai kisaran berat 8,79,1 gram, dengan berat rata-rata 8,8 gram. Padat penebaran yang digunakan adalah 1,67, 2,50, dan 3,33 kg/m², masing-masing mempunyai 3 ulangan. Sebagai rancangan percobaan digunakan latin square. Setiap 2 minggu dilakukan pengamatan berat populasi dan jumlah individu yang menyusun populasi tersebut. Selain itu diamati juga beberapa sifat fisika dan kimia air bagian permukaan yang meliputi suhu, oksigen terlarut, karbon dioksida bebas, derajat keasaman, dan amoniak.
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