Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen
Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is...
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| Format: | Article |
| Language: | Indonesian |
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Indonesian Society of Applied Science (ISAS)
2023-07-01
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| Series: | Journal of Applied Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/524 |
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| _version_ | 1849774292622901248 |
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| author | Sharazita Dyah Anggita Ferian Fauzi Abdulloh |
| author_facet | Sharazita Dyah Anggita Ferian Fauzi Abdulloh |
| author_sort | Sharazita Dyah Anggita |
| collection | DOAJ |
| description |
Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.
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| format | Article |
| id | doaj-art-39bae5bba019442a857f695257613ec2 |
| institution | DOAJ |
| issn | 2723-1453 |
| language | Indonesian |
| publishDate | 2023-07-01 |
| publisher | Indonesian Society of Applied Science (ISAS) |
| record_format | Article |
| series | Journal of Applied Computer Science and Technology |
| spelling | doaj-art-39bae5bba019442a857f695257613ec22025-08-20T03:01:46ZindIndonesian Society of Applied Science (ISAS)Journal of Applied Computer Science and Technology2723-14532023-07-014110.52158/jacost.v4i1.524524Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen Sharazita Dyah Anggita0Ferian Fauzi Abdulloh1Universitas Amikom Yogyakarta IndonesiaUniversitas AMIKOM Yogyakarta Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis. https://journal.isas.or.id/index.php/JACOST/article/view/524Information GainSVMPSO |
| spellingShingle | Sharazita Dyah Anggita Ferian Fauzi Abdulloh Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen Journal of Applied Computer Science and Technology Information Gain SVM PSO |
| title | Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen |
| title_full | Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen |
| title_fullStr | Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen |
| title_full_unstemmed | Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen |
| title_short | Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen |
| title_sort | optimasi algoritma support vector machine berbasis pso dan seleksi fitur information gain pada analisis sentimen |
| topic | Information Gain SVM PSO |
| url | https://journal.isas.or.id/index.php/JACOST/article/view/524 |
| work_keys_str_mv | AT sharazitadyahanggita optimasialgoritmasupportvectormachineberbasispsodanseleksifiturinformationgainpadaanalisissentimen AT ferianfauziabdulloh optimasialgoritmasupportvectormachineberbasispsodanseleksifiturinformationgainpadaanalisissentimen |