A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification
The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2019-07-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/41302 |
| _version_ | 1818197172359790592 |
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| author | Styawati Styawati Khabib Mustofa |
| author_facet | Styawati Styawati Khabib Mustofa |
| author_sort | Styawati Styawati |
| collection | DOAJ |
| description | The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%. |
| first_indexed | 2024-12-12T01:45:45Z |
| format | Article |
| id | doaj.art-785ed9839fd443eabc41ce5ec98a8e97 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-12T01:45:45Z |
| publishDate | 2019-07-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-785ed9839fd443eabc41ce5ec98a8e972022-12-22T00:42:36ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582019-07-0113321923010.22146/ijccs.4130224709A Support Vector Machine-Firefly Algorithm for Movie Opinion Data ClassificationStyawati Styawati0Khabib Mustofa1Department of Information System, FTIK Universitas Teknokrat Indonesia, LampungDepartement of Computer Science and Electronics, FMIPA UGM, YogyakartaThe sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.https://jurnal.ugm.ac.id/ijccs/article/view/41302optimizationclassificationsvmfa-svm |
| spellingShingle | Styawati Styawati Khabib Mustofa A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification IJCCS (Indonesian Journal of Computing and Cybernetics Systems) optimization classification svm fa-svm |
| title | A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| title_full | A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| title_fullStr | A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| title_full_unstemmed | A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| title_short | A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| title_sort | support vector machine firefly algorithm for movie opinion data classification |
| topic | optimization classification svm fa-svm |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/41302 |
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