Judul: Optimisasi Parameter Support Vector Machine Menggunakan Algoritme Particle Swarm Optimization
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 particle swarm optimization (PSO) to search for the kernel parameters with the best accuracy. We use data from UCI repository of machine learning database: Diabetes, Image Letter Recognition and Yeast. The results indicate that the combination of SVM and PSO is effective in improving the accuracy of classification. PSO 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: 78.26% for Diabetes, 97.07% for Letter and 58.21% for Yeast. However, for bigger data sizes, this method may become impractical due to the high time requirement.
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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: Amfibi sebagai satwa peliharaan: Ekspor, Impor dan Perdagangan Domestik
Abstrak: Indonesia has exported amphibian as frozen frog legs (listed since 1969 under fishery product) for consumption and live amphibian (listed since 1983 under reptilian class in forestry product) for pets. There are also live amphibian trading in local level i.e in markets and malls. This research aims to identify amphibian species exported and imported by investigating document from related administration and to identify amphibian species traded in domestic market by observation at Jakarta. Research is primarily conducted by processing quota data taken from Department of Forestry Republic of Indonesia database in Jakarta. Market observation was carried out in 18 locations (3 markets and 15 malls) in Jakarta. I carried out interview with trader and export business, which related to net trading live amphibians for pets. Respondent was choosen using snowball sampling manner. Name of species is written according to Iskandar & Coljin (2000). If there is no such record in the checklist (for example species are from outside Indonesia), then the species name is written according to Frost (2004). There are 56 species from eight families of wild amphibians that have pets quota between 1998-2008. Nineteen species was traded since 1983. The highest export quota is Litoria caerulea (21,37%). Allocation of highest catching quota is for BKSDA Jayapura (20,40%) and BKSDA Papua Barat (18,96%). Four species are included in IUCN Red List categories as vulnerable, among others are Limnonectes macrodon, Meristogenys jerboa, Nyctixalus margaritifer and Rhacophorus margaritifer. The number of amphibians that have export realization are 39 species. Two species of amphibian, Leptobrachium hasseltii and Rhacophorus margaritifer are amphibian species whose been known infected by chytrid fungus in the wild. The biggest species exported is Litoria caerulea (54,34%). Between 1983-1991, the biggest import amphibian for pets from Indonesia (66,13%) is Netherlands. Number of quota was relative stable in the last eight years, where as the number of export realization is more fluctuates. Amphibian species imported and re-exported between 1983-2008 come from Africa, South Asia, Malaysian, Archipelago Oceania, North America, Central America and South America. The number of pet amphibian species traded at domestic market is less than exported. Data showed that Indonesia imports 15 amphibian species between 1983-2008, However I found different species in the market that was unrecorded in the Ministry of Forestry data, which is the Fire Belly Salamander.
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