{"id":1985,"date":"2017-09-11T15:10:15","date_gmt":"2017-09-11T08:10:15","guid":{"rendered":"http:\/\/research.binus.ac.id\/bdsrc\/?p=1985"},"modified":"2020-10-26T11:32:25","modified_gmt":"2020-10-26T04:32:25","slug":"peramalan-crude-palm-oil-cpo-menggunakan-support-vector-regression-kernel-radial-basis","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/bdsrc\/2017\/09\/11\/peramalan-crude-palm-oil-cpo-menggunakan-support-vector-regression-kernel-radial-basis\/","title":{"rendered":"Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis"},"content":{"rendered":"<p style=\"text-align: justify\">Recently, instead of selecting a kernel has been proposed which uses\u00a0SVR, where the weight of each kernel is optimized during training. Along this line\u00a0of research, many pioneering kernel learning algorithms have been proposed. The\u00a0use of kernels provides a powerful and principled approach to modeling nonlinear\u00a0patterns through linear patterns in a feature space. Another benefit is that the design\u00a0of kernels and linear methods can be decoupled, which greatly facilitates the modularity<br \/>\nof machine learning methods. We perform experiments on real data sets\u00a0crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values\u00a0showing the validity and accuracy of the realized model based on MAPE and\u00a0R2.<\/p>\n<p>Jurnal Matematika Universitas Udayana<\/p>\n<p><strong>Rezzy Eko Caraka, Hasbi Yasin, Adi Waridi Basyiruddin<\/strong><\/p>\n<p><a href=\"https:\/\/ojs.unud.ac.id\/index.php\/jmat\/article\/view\/30836\">Rea Full Paper<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recently, instead of selecting a kernel has been proposed which uses\u00a0SVR, where the weight of each kernel is optimized during training. Along this line\u00a0of research, many pioneering kernel learning algorithms have been proposed. The\u00a0use of kernels provides a powerful and principled approach to modeling nonlinear\u00a0patterns through linear patterns in a feature space. Another benefit is [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":3018,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-1985","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/1985","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/comments?post=1985"}],"version-history":[{"count":4,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/1985\/revisions"}],"predecessor-version":[{"id":2937,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/1985\/revisions\/2937"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media\/3018"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media?parent=1985"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/categories?post=1985"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/tags?post=1985"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}