Judul: Studi Toksisitas Nikel (Ni) terhadap Konsumsi Oksigen, Kondisi Hematologi, Histopatologi dan Stres Sekunder Juvenil Ikan Bandeng Chanos chanos [Forsskal]
Abstrak: Nikel merupakan salah satu jenis logam berat yang memiliki sifat toksik. Logam berat nikel dapat meracuni darah, menganggu sistem pernapasan, merusak jaringan, selaput lendir, dan mengubah sistem sel. Kegagalan produksi budidaya ikan pada perairan sekitar industri pertambangan nikel berpotensi disebabkan oleh nikel. Hal ini dimungkinkan dengan peningkatan kekeruhan pada perairan pesisir di lokasi pertambangan nikel. Perairan pesisir dengan potensi kandungan logam berat nikelnya pada akhirnya masuk ke petakan tambak ikan bandeng bersamaan dengan pasang air laut atau melalui pemompaan. Penelitian ini bertujuan untuk mengetahui potensi toksisitas akut nikel yang diekspresikan oleh nilai LC50 nikel dan mengkaji pengaruh toksisitas nikel terhadap tingkat konsumsi oksigen, kondisi hematologi, histopatologi dan stres sekunder juvenil ikan bandeng. Penelitian ini dilaksanakan atas tiga tahap, yaitu: uji nilai kisaran, uji akut, dan uji sub-kronik. Ikan uji yang digunakan adalah ikan bandeng ukuran 6-7 cm, sedangkan logam berat nikel yang digunakan sebagai sumber toksikan dalam penelitian adalah nikel klorida (NiCl2 Hasil penelitian menunjukkan bahwa nikel memiliki sifat toksik yang tinggi terhadap juvenil ikan bandeng dengan nilai LC). Wadah penelitian berupa akuarium berukuran 20 x 20 x 30 cm, yang diisi air laut dengan salinitas 15 ‰ sebanyak 10 liter. Analisis kadar nikel pada media percobaan menggunakan Atomic Absorption Spectroscopy (AAS). Pada uji akut, digunakan 5 taraf konsentrasi yaitu tanpa nikel (A); 8,89 ppm (B); 15,81 ppm (C); 28,12 ppm (D) dan 50,01 ppm (E). Selanjutnya pada uji sub-kronis, digunakan 5 taraf konsentrasi yaitu 0 ppm; 0,12 ppm; 0,59 ppm; 1,19 ppm dan 3,56 ppm.
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Judul: Toksisitas nikel [ni] terhadap ikan nila gift (Oreochromis niloticus) pada media berkesadahan lunak (soft hardnes)
Abstrak: Nickel is a member of heavy metals. It can potentially poison the blood, interfere the respiratory system, damage tissues and membranes of lenders, and change the cell system. This research aimed to determine the effect of nickel toxicity on various biochemical processes in body GIFT tilaphia such as : level of oxygen consumption, hematological system, histopathology system, level of nickel accumulation,growth rate, and on survival rate. This research was conducted in three stages: test value range, acute test, and sub-chronic test. Acute test consisted of 5 treatment concentrations (0.00, 10.67, 18.98, 33.76, and 60.05 ppm). Sub-chronic test consisted of 3 phases (0.00, 1.39, and 4.18 ppm). Lc-50 value of 96 hours was 13.93 ppm. The level of nickel accumulations in flesh on treatment with concentrations of 60.05, 33.76, 18.98, 10.67, and 00:00 ppm respectively were: 73.37, 56.08, 42.00, and 32.90 mg/kg, while at 0:00 ppm, nickel was not detected. Generally, level of oxygen consumption in chronic sub test decreased significantly. After 32 days nickel exposure, treatment with concentration 4,18 and 1,39 ppm was respectively down: 0,47 to 0,14, and 0,46 to 0,21. At 0,00 ppm, it also decreased level of oxygen consumption but it was still stable relatively, from 0,49 to 0,42. Nickel exposure for 30 days also decreased hematocrit level, hemoglobin, erythrocyte, increased number of leukocytes, reduced growth rate, and damaged gill and liver
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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