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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | EJournal Article |
| Published: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2019-07-31.
|
| Subjects: | |
| Online Access: | Get Fulltext Get Fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02470 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_41302 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Styawati, Styawati |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Mustofa, Khabib |e author |
| 245 | 0 | 0 | |a A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2019-07-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/41302 | ||
| 520 | |a 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%. | ||
| 540 | |a Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Optimization; Classification; SVM; FA-SVM | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 3 (2019): July; 219-230 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/41302/25081 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/41302 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/41302/25081 |z Get Fulltext |