NDLI: Analisis Sentimen Masyarakat pada Media Sosial Twitter Terhadap Partai Politik Peserta Pemilihan Umum 2019 Menggunakan Naive Bayes Classifier

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Analisis Sentimen Masyarakat pada Media Sosial Twitter Terhadap Partai Politik Peserta Pemilihan Umum 2019 Menggunakan Naive Bayes Classifier

Content Provider	Semantic Scholar
Author	Adiati, Aprillia Rizki
Copyright Year	2019
Abstract	Abstrak Menjelang Pemilihan Umum 2019, banyak partai politik memanfaatkan media sosial untuk berkampanye dan meningkatkan popularitas. Salah satu media sosial yang banyak digunakan dalam media promosi partai politik adalah twitter. Selain itu, media sosial twitter juga dapat dijadikan tempat oleh masyarakat dalam memberikan opini terhadap partai terkait baik opini positif maupun opini negatif. Pada tugas akhir ini dibuat untuk menganalisis opini masyarakat terhadap partai politik peserta pemilu 2019 menggunakan metode Naive Bayes Classifier. Berdasarkan sistem yang dibangun, didapatkan hasil sentimen positif sebesar 53,8% dan sentimen negatif 46,13% dengan rata-rata akurasi sebesar 78,03%. Kata kunci: pemilihan umum, partai politik, analisis sentimen, Twitter, Naive Bayes Classifier. Abstract Ahead of the 2019 general election, many political parties used social media to campaign and increase popularity. One of the social media that is widely used in the media promotion of political parties is Twitter. In addition, social media twitter can also be used as a place by the public in providing opinions to parties related to both positive and negative opinions. In this final project, it is made to analyze public opinion towards political parties participating in the 2019 elections using the Naive Bayes Classifier method. Based on the system that was built, obtained an average accuracy of 78.03% and the results of positive sentiment of 53.8% and negative sentiment of 46.13%. Keywords: election, political parties, sentiment analysis, Twitter, Naive Bayes Classifier
File Format	PDF HTM / HTML
Volume Number	6
Alternate Webpage(s)	https://libraryeproceeding.telkomuniversity.ac.id/index.php/engineering/article/download/9836/9697
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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