Judul: Algoritme Genetika untuk Optimasi Pembobotan Fitur pada Temu Kembali Citra
Abstrak: Content based image retrieval is developed to retrieve image based on color, shape, and texture feature. One of the problems to be solved in content based image retrieval is how to combine these features to get the optimum retrieval result. Combining multiple features in image retrieval process can be implemented using feature weight assignment. One of feature weight assignment methods is each feature weight is assigned by hand. However this approach is subjective and lack of theory foundation. In this research an automatic feature weight assignment approach based on genetic algorithm is proposed. Genetic algorithm is used for optimizing feature weight in order to get the optimum retrieval result. Image features is used in this research are color, shape, and texture feature. In genetic algorithm, a chromosom represented by features weight. These chromosomes get into genetic operator process such as selection, crossover, and mutation until maximum generation is reached. Result of genetic algorithm process get an optimum feature weight. It used for image retrieval process. The retrieval evaluation used recall precision graphic. The experiment showed feature weight assignment based on genetic algorithm can improve average precision in image retrieval.
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Judul: Optimisasi Parameter Support Vector Machine (SVM) Menggunakan Algoritme Genetika
Abstrak: Support vector machine (SVM) is a popular classification method which is known to have a robust generalization capability. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. Kernel trick uses various kinds of kernel functions, such as: linear kernel, polynomial, radial basis function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. In this research, we propose a combination of the SVM algorithm and genetic algorithm to search for the kernel parameters with the best accuracy. We use data from UCI repository of machine learning database: Image Letter Recognition, Diabetes, and Yeast. The results indicate that the combination of SVM and genetic algorithm is effective in improving the accuracy of classification. Genetic algorithm has been shown to be effective in systematically finding optimal kernel parameters for SVM, as a replacement for randomly choosing kernel parameters. Best accuracy for each data has been improved from previous research: 97.05% for Letter, 78.21% for Diabetes, and 58.97% for Yeast. However, for bigger data sizes, this method may become impractical due to the high time requirement.
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Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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