Judul: Penambahan asam humik pada kondisi salin terhadap pertumbuhan Cynodon dactylon varietas tifdwarf
Abstrak: The research was conducted in laboratory of Agrostology greenhouse, Faculty of Animal Science, Bogor Agricultural University. The purpose of this research are to measure cynodon dactylon tolerance in different soil salinity and the affect increasing humic acid volume to soil salinity to Cynodon dactylon growth. The experiment units are arranged in a completely randomized factorial design 3x4 with 3 replications by using Cynodon dactylon (Bermuda grass).
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Judul: Ketahanan rumput golf (Cynodon dactylon var. Tift dwarft 419) pada kondisi salinitas yang berbeda dengan penambahan asam humat
Abstrak: The objectives of this research was study the effects of salinity and humic acid on growth, production and quality of Cynodon dactylon. The experimental units were arranged in a completely randomized factorial design 2x5 with 3 replications using Cynodon dactylon as host plant. The first factor was huinic acid, consisting of HO (without humic acid) and Hl(with humic acid) and the second factor was the level of salinity consisting of SO (0 ppm); S1 (2500 ppm); S2 (5000 ppm); S3 (7500 ppm); S4 (10000 pprn). Analyses of Variance, Duncan Multiple Range Tests were used to analyze and assist in interpretation of the data. Result showed that salinity very significantly different (P<0.01) on color of leaf, percentage death, vertical high plant and biomass dry weight. Humic acid significantly different (P<O.OOj) on vertical high plant. Humic acid didn't significantly different on color of leaf, density, total an individual, percentage of death, biomass dry weight and root dry weight. Salinity didn't significantly different on density, total an individual and root dry weight.
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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