Judul: Manipulasi Fotothermal Dalam Memacu Pematangan Gonad Ikan Senggaringan (Mystus nigriceps)
Abstrak: Fotoperiode, temperatur dan curah musim hujan adalah beberapa faktor lingkungan yang sangat penting dalam mengatur siklus reproduksi ikan. Di alam, faktor lingkungan menentukan sebagain besar pemilihan waktu reproduksi dan strategi reproduksi jenis ikan-ikan tertentu. Beberapa penelitian terhadap spesies subtropis memperlihatkan adanya dampak positif terhadap reproduksi ikan. Scott (1979) menambahkan bahwa faktor lingkungan yang dominan mempengaruhi perkembangan gonad adalah temperatur dan makanan termasuk periode pencahayaan dan musim, kemudian ditambahkan bahwa periode penyinaran yang rendah dan temperatur yang tinggi dapat mempercepat pematangan gonad.
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Judul: Photothermal Manipulation on Advance of Gonadal Maturation in the Senggaringan (Mystus nigriceps)
Abstrak: Photothermal has been considered one of the most important factors triggering puberty, as well as reproduction, in several fish species, including Senggaringan. Senggaringan is one of the wild fish living in the Klawing river and high economic value and in the future it is expected to become one of aquaculture commodities. As the activity of fishing has done constantly and begun to cause fish habitat damage, the fish has been rare in the market even catches by fishermen tend to decrease. To maintain the existence of this fish, there needs domestication efforts ultimately leading to conservation. This research was laboratory experiment using completely randomized factorial design to apply nine different photothermal treatments, namely: 1) natural temperature and duration of natural lighting (T0L0), 2) natural temperature and duration of lighting 10L:14D (T0L1), 3) natural temperature and duration of lighting 14L:10D (T0L2), 4) temperature of 25oC and duration of natural lighting (T1L0), 5) temperature of 25oC and duration of exposure 10L:14D (T1L1), 6) temperature of 25oC and duration of exposure 14L:10D (T1L2), 7) temperature of 30oC and duration of natural lighting (T2L0), 8) temperature of 30oC and duration of exposure 10L:14D (T2L1) and 9) temperature of 30oC and duration of exposure 14L:10D (T2L2). Results showed among the photothermal treatments tested that there were two treatments supporting the development of the fish ovary. The first, the GSI values in T0L1 treatment continuously increased until the end of the experiment from 5.7% to 15% (highest). However, statistically there was no significantly different among treatments (P>0.05, P=0.077). The results of this research also showed that an increase in GSI value of 15% (T0L1) caused to lower values for HSI 1.3% (P>0.05, P=0.061) and VSI 1.6% (P>0.05, P=0.058). The second, in T1L0 treatment, highest GSI value was achieved in August by 13.3% from 7.1%. There also occurred a decrease in HSI value of 1.2% and value VSI of 2.6%. The value of the GSI before photothermal was 3.7%. This profile showed the negative relationship between the GSI and HSI, and also negative relationship between GSI and VSI with the meaning which the increase in the GSI values declined in values of HSI and VSI. Based on the proportion of oogenesis phase cells showed that late vitellogenesis stage for the highest was achieved about 41.2% in the treatment T1L0. However, based on statistical tests there was no significant difference among the treatments (P>0.05, P=0.2). The value of final vitellogenesis before photothermal was 10.2%. Thus, it can be concluded that the gonadal development of the fish female under the photothermal manipulation has not shown maximum performance. It seems that there is needed to extent exposure of lighting to affect its gametogenesis.
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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