Levels of Political Participation Based on Naive Bayes Classifier
Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has bee...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2019-01-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/42531 |
| _version_ | 1818244636645261312 |
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| author | Rumaisah Hidayatillah Mirwan Mirwan Mohammad Hakam Aryo Nugroho |
| author_facet | Rumaisah Hidayatillah Mirwan Mirwan Mohammad Hakam Aryo Nugroho |
| author_sort | Rumaisah Hidayatillah |
| collection | DOAJ |
| description | Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said. |
| first_indexed | 2024-12-12T14:20:10Z |
| format | Article |
| id | doaj.art-0e35a8667a2b42869ca931b9076819c4 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-12T14:20:10Z |
| publishDate | 2019-01-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-0e35a8667a2b42869ca931b9076819c42022-12-22T00:21:48ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582019-01-01131738210.22146/ijccs.4253123378Levels of Political Participation Based on Naive Bayes ClassifierRumaisah Hidayatillah0Mirwan Mirwan1Mohammad Hakam2Aryo Nugroho3Department of Informatics Engineering, Universitas Narotama, SurabayaDepartment of Informatics Engineering, Universitas Narotama, SurabayaDepartment of Computer Systems, Universitas Narotama, SurabayaFaculty of Computer Science, Universitas Narotama, Surabaya, IndonesiaNowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.https://jurnal.ugm.ac.id/ijccs/article/view/42531social mediaelection campaignnaïve bayes |
| spellingShingle | Rumaisah Hidayatillah Mirwan Mirwan Mohammad Hakam Aryo Nugroho Levels of Political Participation Based on Naive Bayes Classifier IJCCS (Indonesian Journal of Computing and Cybernetics Systems) social media election campaign naïve bayes |
| title | Levels of Political Participation Based on Naive Bayes Classifier |
| title_full | Levels of Political Participation Based on Naive Bayes Classifier |
| title_fullStr | Levels of Political Participation Based on Naive Bayes Classifier |
| title_full_unstemmed | Levels of Political Participation Based on Naive Bayes Classifier |
| title_short | Levels of Political Participation Based on Naive Bayes Classifier |
| title_sort | levels of political participation based on naive bayes classifier |
| topic | social media election campaign naïve bayes |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/42531 |
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