Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke
Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting...
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| Format: | Article |
| Language: | Indonesian |
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Indonesian Society of Applied Science (ISAS)
2024-12-01
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| Series: | Journal of Applied Computer Science and Technology |
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| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/817 |
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| author | Danis Rifa Nurqotimah Ahsanun Naseh Khudori Risqy Siwi Pradini |
| author_facet | Danis Rifa Nurqotimah Ahsanun Naseh Khudori Risqy Siwi Pradini |
| author_sort | Danis Rifa Nurqotimah |
| collection | DOAJ |
| description |
Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. The study used data sets from the data.world.com site with a total of 40,910 data. Using the Orange tool, the study managed to classify stroke disease well using the RBF kernel with cross validation techniques resulting in an accuracy of 94.8%. The results of this study can be concluded that the stroke classification model developed has excellent performance. Overall, these results indicate that the Stroke classification model developed is highly reliable and effective, with excellent ability to detect stroke cases and provide accurate predictions. Making better and quicker medical judgments can be aided by using this approach to diagnose strokes.
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| format | Article |
| id | doaj-art-9ef3d0935f81496ea412880f462993f3 |
| institution | DOAJ |
| issn | 2723-1453 |
| language | Indonesian |
| publishDate | 2024-12-01 |
| publisher | Indonesian Society of Applied Science (ISAS) |
| record_format | Article |
| series | Journal of Applied Computer Science and Technology |
| spelling | doaj-art-9ef3d0935f81496ea412880f462993f32025-08-20T03:01:46ZindIndonesian Society of Applied Science (ISAS)Journal of Applied Computer Science and Technology2723-14532024-12-015210.52158/jacost.v5i2.817817Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit StrokeDanis Rifa Nurqotimah0Ahsanun Naseh Khudori1Risqy Siwi Pradini2Institut Teknologi, Sains, dan Kesehatan RS.DR.Soepraoen Kesdam V/BRWInstitut Teknologi, Sains, dan Kesehatan RS. DR. Soepraoen Kesdam V/BRWInstitut Teknologi, Sains, dan Kesehatan RS. DR. Soepraoen Kesdam V/BRW Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. The study used data sets from the data.world.com site with a total of 40,910 data. Using the Orange tool, the study managed to classify stroke disease well using the RBF kernel with cross validation techniques resulting in an accuracy of 94.8%. The results of this study can be concluded that the stroke classification model developed has excellent performance. Overall, these results indicate that the Stroke classification model developed is highly reliable and effective, with excellent ability to detect stroke cases and provide accurate predictions. Making better and quicker medical judgments can be aided by using this approach to diagnose strokes. https://journal.isas.or.id/index.php/JACOST/article/view/817stroke, support vector machine, klasifikasi |
| spellingShingle | Danis Rifa Nurqotimah Ahsanun Naseh Khudori Risqy Siwi Pradini Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke Journal of Applied Computer Science and Technology stroke, support vector machine, klasifikasi |
| title | Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke |
| title_full | Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke |
| title_fullStr | Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke |
| title_full_unstemmed | Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke |
| title_short | Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke |
| title_sort | implementasi algoritma support vector machine svm untuk klasifikasi penyakit stroke |
| topic | stroke, support vector machine, klasifikasi |
| url | https://journal.isas.or.id/index.php/JACOST/article/view/817 |
| work_keys_str_mv | AT danisrifanurqotimah implementasialgoritmasupportvectormachinesvmuntukklasifikasipenyakitstroke AT ahsanunnasehkhudori implementasialgoritmasupportvectormachinesvmuntukklasifikasipenyakitstroke AT risqysiwipradini implementasialgoritmasupportvectormachinesvmuntukklasifikasipenyakitstroke |