Levels of Political Participation Based on Naive Bayes Classifier
| Autor: | Mirwan Mirwan, Rumaisah Hidayatillah, Aryo Nugroho, Mohammad Hakam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Informatics Engineering
Java Computer science Microblogging social media 0211 other engineering and technologies 050801 communication & media studies naïve bayes 02 engineering and technology Crawling lcsh:QA75.5-76.95 Naive Bayes classifier Politics 0508 media and communications Social media election campaign computer.programming_language Information retrieval 05 social sciences lcsh:Q300-390 021107 urban & regional planning Variable (computer science) Public participation lcsh:Electronic computers. Computer science lcsh:Cybernetics computer |
| Zdroj: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 1 (2019): January; 73-82 IJCCS (Indonesian Journal of Computing and Cybernetics Systems), Vol 13, Iss 1, Pp 73-82 (2019) |
| ISSN: | 1978-1520 2460-7258 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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