Judul: Pengaruh penambahan zeolit dalam pengangkutan sistem tertutup benih ikan gurame (Osphronemus goramy, Lac.)
Abstrak: Penelitian ini dilaksanakan di Balai Penelitian Peri- kanan Air Tawar (Balitkanwar), Bogor pada tanggal 28-29 Maret 1989. Penelitian ini dilakukan dengan tujuan melihat pengaruh pemberian beberapa tingkat dosis zeolit pada berbagai tingkat kepadatan terhadap kelangsungan hidup benih ikan gurame selama proses pengangkutan 24 jam. Rancangan percobaan yang digunakan adalah Rancangan Acak Faktorial dengan 2 faktor yaitu kepadatan dan dosis zeolit. Kepadatan ikan yang digunakan adalah 10, 15, 20, 25 ekor/liter, sedangkan dosis zeolit yang digunakan adalah 0, 65, 130, 195 gram/liter dan masing-masing perlakuan terdiri dari 3 ulangan. Wadah pengangkutan ialah plastik polietilen berukuran tebal 0.08 mm, lebar 50 cm dan panjang 60 cm dan dibuat rangkap dua. Pada waktu pengemasan diisi air 6 liter dan oksigen yang perbandingan antara oksigen dengan air 2 1, kemudian dimasukkan ke dalam kotak styrofoam dan disusun dalam truk kemudian dilapisi kanvas. Ikan yang digunakan berukuran antara 8 21 gram dan zeolit yang dipakai adalah zeolit buatan produksi PT. Batumas Primamuda dengan merek dagang "Batu Mas Zeolit" berukuran 28 48 mesh.
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Judul: Effectiveness of the Addition of Salt in Closed Transport Media, Containing Zeolite 20 g/l and Activated Carbon 10 g/l, Gurame Fish Osphronemus gouramy Lac. With High density
Abstrak: Pengangkutan ikan biasanya dilakukan dengan kepadatan yang tinggi untuk mengefisiensikan biaya transportasi, namun semakin padat ikan yang dibawa dalam suatu wadah maka akan meningkatkan stres pada ikan. Stres yang timbul akan mengakibatkan aktifitas fisiologis ikan meningkat yang dapat memperburuk kualitas air khusunya DO, CO2, dan NH3. Penelitian ini bertujuan untuk mengevaluasi efektifitas penambahan garam pada media yang telah diberi zeolit dan karbon aktif dalam mempertahankan kualitas air media pengangkutan, sehingga dapat meminimalisasi tingkat kematian pada pengangkutan tertutup benih gurame ukuran ±4 cm. Penelitian ini dilakukan dalam skala laboratorium dengan rancangan acak lengkap. Ikan gurame Osphronemus gouramy yang diuji pada penelitian ini diberi empat perlakuan yang berbeda dan dengan tiga ulangan. Perlakuan tersebut yaitu A) 50 ekor/ℓ + 20 g/ℓ zeolit + 10 g/ℓ karbon aktif, 4 g/ℓ garam; B) 50 ekor/ℓ + 20 g/ℓ zeolit + 10 g/ℓ karbon aktif, 6 g/ℓ garam; C) 50 ekor/ℓ + 20 g/ℓ zeolit + 10 g/ℓ karbon aktif, 8 g/ℓ garam;dan D) 50 ekor/ℓ + 20 g/ℓ zeolit + 10 g/ℓ karbon aktif, 10 g/ℓ garam. Pengangkutan dilakukan selama 72 jam. Hasil penelitian menunjukkan tingkat kelangsungan hidup selama pengangkutan untuk perlakuan A, B, C, dan D masing-masing 86%, 35,33%, 16,67%, dan 5,33%. Hasil penelitian ini menunjukkan perlakuan A memberikan hasil terbaik dilihat dari nilai tingkat kelangsungan hidup (SR) sebesar 86 %, yang dihubungkan dengan nilai kualitas air media pengangkutan yang baik dimana kadar total amoniak nitrogen (TAN) 0.87±0.03, NH3 0.03535±0.005, DO 4.8033 mg/ℓ serta memiliki laju pertumbuhan harian yang paling besar yakni 4,73%, dan tingkat kelangsungan hidup selama pemeliharaan pasca pengangkutan sebesar 100%.
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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