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: 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: Distribusi suhu, salinitas dan oksigen terlarut di Laut Arafura Selatan pada bulan November 1992
Abstrak: Tujuan penelitian ini adalah (1) mengetahui variabilitas sebaran suhu, salinitas dan oksigen terlarut secara mendatar dan menegak, (2) Mengidentifikasi massa air di Laut Arafura Selatan. Data yang digunakan dalam penelitian ini adalah data Ekspedisi MINA ZEE-06 1992 pada bulan November 1992. Daerah yang diteliti adalah perairan Laut Arafura bagian selatan yang termasuk Zona Ekonomi Eksklusif Indonesia (ΖΕΕΙ). Suhu dan konduktivitas air laut diukur dengan menggu- nakan CTD Guildline 6000. Nilai suhu ditentukan dengan persamaan salinitas dan konduktivitas. Pengukuran dilakukan di 18 stasiun oseanografi dari permukaan hingga dekat dasar. Sampel air laut untuk menentukan kadar oksigen terlarut diambil dengan botol Niskin pada kedalaman "stan- dar". Kadar oksigen terlarut dianalisa dengan menggunakan metode Winkler. Data menunjukkan bahwa suhu lapisan permukaan berkisar antara 27.72 28.47 °C dengan rata-rata 28.10 °C, dan nilai yang lebih tinggi terdapat di bagian barat. Sebaran suhu secara mendatar memperlihatkan adanya kecenderungan suhu lebih tinggi di daerah barat.
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