BibTex Citation Data :
@article{J.Gauss8086, author = {Mekar Sari and Diah Safitri and Sugito Sugito}, title = {KLASIFIKASI WILAYAH DESA-PERDESAAN DAN DESA-PERKOTAAN WILAYAH KABUPATEN SEMARANG DENGAN SUPPORT VECTOR MACHINE (SVM)}, journal = {Jurnal Gaussian}, volume = {3}, number = {4}, year = {2014}, keywords = {}, abstract = { This research will be carry out classification based on the status of the rural and urban regions that reflect the differences in characteristics/ conditions between regions in Indonesia with Support Vector Machine (SVM) method. Classification on this issue is working by build separation functions involving the kernel function to map the input data into a higher dimensional space. Sequential Minimal Optimization (SMO) algorithms is used in the training process of data classification of rural and urban regions to get the optimal separation function (hyperplane). To determine the kernel function and parameters according to the data, grid search method combined with the leave-one-out cross-validation method is used. In the classification using SVM, accuracy is obtained, which the best value is 90% using Radial Basis Function (RBF) kernel functions with parameters C=100 dan γ=2 -5 . Keywords : classification, support vector machine, sequential minimal optimization, grid search, leave-one-out, cross validation, rural, urban }, issn = {2339-2541}, pages = {751--760} doi = {10.14710/j.gauss.3.4.751-760}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/8086} }
Refworks Citation Data :
This research will be carry out classification based on the status of the rural and urban regions that reflect the differences in characteristics/ conditions between regions in Indonesia with Support Vector Machine (SVM) method. Classification on this issue is working by build separation functions involving the kernel function to map the input data into a higher dimensional space. Sequential Minimal Optimization (SMO) algorithms is used in the training process of data classification of rural and urban regions to get the optimal separation function (hyperplane). To determine the kernel function and parameters according to the data, grid search method combined with the leave-one-out cross-validation method is used. In the classification using SVM, accuracy is obtained, which the best value is 90% using Radial Basis Function (RBF) kernel functions with parameters C=100 dan γ=2-5.
Keywords : classification, support vector machine, sequential minimal optimization, grid search, leave-one-out, cross validation, rural, urban
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