Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine
The Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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UIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering Department
2024-08-01
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| Series: | Journal of Industrial Engineering and Halal Industries |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/4672 |
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| author | Rizki Rahmatullah Jundi Nourfateha Elquthb Fanya Nindha Al-Qurani Annisa Uswatun Khasanah |
| author_facet | Rizki Rahmatullah Jundi Nourfateha Elquthb Fanya Nindha Al-Qurani Annisa Uswatun Khasanah |
| author_sort | Rizki Rahmatullah |
| collection | DOAJ |
| description | The Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged that this program can reduce the transmission of viruses in monitoring. Indonesians have used the PeduliLindungi, and there are user reviews of both positive and negative experiences. Therefore, to enhance these services, an assessment is required. The text mining method can extract information from users' reviews to collect this data. This method's application additionally uses the Naive Bayes Classifier and Support Vector Machine algorithms, which analyze word associations and do a classification evaluation of the data's accuracy. Based on the two methods' calculations, the NBC algorithm's average classification accuracy was 83.81%, and the SVM algorithm was 93.84%. Following that, discoveries on words that frequently exist or are used by people are obtained through word associations in the sentiment analysis of positive or negative reviews. |
| format | Article |
| id | doaj-art-34ed28e281b8427098416d5d557f0520 |
| institution | Kabale University |
| issn | 2722-8150 2722-8142 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | UIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering Department |
| record_format | Article |
| series | Journal of Industrial Engineering and Halal Industries |
| spelling | doaj-art-34ed28e281b8427098416d5d557f05202025-01-06T05:54:18ZengUIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering DepartmentJournal of Industrial Engineering and Halal Industries2722-81502722-81422024-08-0151364210.14421/jiehis.46724296Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector MachineRizki Rahmatullah0Jundi Nourfateha Elquthb1Fanya Nindha Al-Qurani2Annisa Uswatun Khasanah3Universitas Islam IndonesiaUniversitas Islam IndonesiaUniversitas Islam IndonesiaUniversitas Islam IndonesiaThe Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged that this program can reduce the transmission of viruses in monitoring. Indonesians have used the PeduliLindungi, and there are user reviews of both positive and negative experiences. Therefore, to enhance these services, an assessment is required. The text mining method can extract information from users' reviews to collect this data. This method's application additionally uses the Naive Bayes Classifier and Support Vector Machine algorithms, which analyze word associations and do a classification evaluation of the data's accuracy. Based on the two methods' calculations, the NBC algorithm's average classification accuracy was 83.81%, and the SVM algorithm was 93.84%. Following that, discoveries on words that frequently exist or are used by people are obtained through word associations in the sentiment analysis of positive or negative reviews.https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/4672classificationnaïve bayes classifierpedulilindungisentiment analysissupport vector machineword association |
| spellingShingle | Rizki Rahmatullah Jundi Nourfateha Elquthb Fanya Nindha Al-Qurani Annisa Uswatun Khasanah Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine Journal of Industrial Engineering and Halal Industries classification naïve bayes classifier pedulilindungi sentiment analysis support vector machine word association |
| title | Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine |
| title_full | Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine |
| title_fullStr | Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine |
| title_full_unstemmed | Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine |
| title_short | Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine |
| title_sort | sentiment analysis of pedulilindungi user using naive bayes classifier algorithm and support vector machine |
| topic | classification naïve bayes classifier pedulilindungi sentiment analysis support vector machine word association |
| url | https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/4672 |
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