Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination
The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's resp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2022-04-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1090 |
| _version_ | 1797850418850562048 |
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| author | Rahmaddeni Rahmaddeni M. Khairul Anam Yuda Irawan Susanti Susanti Muhammad Jamaris |
| author_facet | Rahmaddeni Rahmaddeni M. Khairul Anam Yuda Irawan Susanti Susanti Muhammad Jamaris |
| author_sort | Rahmaddeni Rahmaddeni |
| collection | DOAJ |
| description | The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting. |
| first_indexed | 2024-04-09T18:59:56Z |
| format | Article |
| id | doaj.art-28d0898ad1104fbfa3b86a3652d7ef72 |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T18:59:56Z |
| publishDate | 2022-04-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-28d0898ad1104fbfa3b86a3652d7ef722023-04-08T08:20:28ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792022-04-01141323810.33096/ilkom.v14i1.1090.32-38388Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 VaccinationRahmaddeni Rahmaddeni0M. Khairul Anam1Yuda Irawan2Susanti Susanti3Muhammad Jamaris4STMIK Amik RiauSTMIK Amik RiauSTMIK Hangtuah PekanbaruSTMIK Amik RiauSTMIK Amik RiauThe corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1090sentiment analysisvaccinationcovid-19svmxgbsvm |
| spellingShingle | Rahmaddeni Rahmaddeni M. Khairul Anam Yuda Irawan Susanti Susanti Muhammad Jamaris Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination Ilkom Jurnal Ilmiah sentiment analysis vaccination covid-19 svm xgbsvm |
| title | Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination |
| title_full | Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination |
| title_fullStr | Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination |
| title_full_unstemmed | Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination |
| title_short | Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination |
| title_sort | comparison of support vector machine and xgbsvm in analyzing public opinion on covid 19 vaccination |
| topic | sentiment analysis vaccination covid-19 svm xgbsvm |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1090 |
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