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: Enteric Collibacillosis Pada Anak Babi Baru Lahir: Patogenesa dan Perubahan-Perubahan Patologisnya
Abstrak: Diare disebabkan oleh infeksi bakteri Escherichia coli pada anak-anak babi baru lahir yang peka terhadap en terotoksin produk bakteri tersebut. Dikenal sebagai colibacillosis. Patogenesa dari colibacillosis anak babi baru lahir adalah (1) infeksi oleh E. coli patogen, (2) proliferasi E. coli di lumen usus halus, (3) produksi dan pelepasan enterotoksin, dan (4) aktifitas toksin pada usus halus. Dalam proses proliferasi, adanya antigen pili K88 sangat penting karena memungkinkan bakteri perlekatan dengan epitel mucosa usus halus. mengadakan Setelah ko lonisasi dan proliferasi segera toksin dilepaskan. Akti- fitas toksin menginduksi sekresi yang berlebihan dari u- sus sehingga banyak cairan yang dikeluarkan ke lumen usus. Hal ini mengakibatkan banyak cairan dan elektrolit hilang sehingga timbul diare, dehidrasi dan asidosis yang sering menyebabkan kematian. Perubahan-perubahan patologis yang umum adalah dis tensi usus halus, dilatasi lambung dan infark pada kurva- tura mayor. Perubahan-perubahan histologis yang terjadi adalah enteritis hemorrhagica acuta, oedem dan kongesti pembuluh darah di lamina propria, akumulassi leukosit inti polimorf, sel monosit, dan inti sel mengalami piknosis Thrombus pada vena dan degenerasi serta desquamasi sel epitel usus….
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