ANALISIS SENTIMEN PADA PEMERINTAHAN TERPILIH PADA PILPRES 2019 DITWITTER MENGGUNAKAN ALGORITME NAÏVEBAYES

Febby Apri Wenando, Regiolina Hayami, Agung Jefrianto Anggrawan

Abstract


Abstract: The Presidential general election on 2019 became one of the most popular topics on twitter nowdays.  The society give their opinion about the  pair of  candidates that they are support through the social media. This research was predicts about the society sentimens toward the candidates of President and Vice President of Republic of Indonesia. The data was used  based on the tweet on the @jokowi twitter account. The retrieval of data by using the Tweepy library with the Python 2.7 programming language. This research was classified became of two of society sentiments classes, namely positive and negative. The modeling was used of the weighting method Unigram, Bigram, Trigram, N-Gram (1-2) and N-Gram (1-3)  that used the Naïve Bayes Algorithm on the Weka Application. The modeling data was used by the dataset of 646 sentences. The highest results  of this reseach were obtained  by Unigram Weighting, namely: 81.4% accuracy, 81.5% precision, 81.3% recall with a time of 0.3 s.

Keywords: classification, naïve bayes, 2019 presidential election, twitter, unigram

 

Abstrak: Pemilihan Umum tentang Pilpres 2019 menjadi salah satu topik yang ramai diperbincangkan di Twitter. Adu pendapat di sosial media oleh masyarakat mengandung opini terhadap pasangan calon yang didukungnya. Penelitian ini memprediksi sentimen masyarakat kepada pasangan calon Presiden dan Wakil Presiden Republik Indonesia. Data yang digunakan adalah tweet yang ada pada akun Twitter @jokowi. Pengambilan data menggunakan library Tweepy dengan bahasa pemrograman Python 2.7. Penelitian ini mengklasifikasi sentimen masyarakat menjadi 2 kelas, yaitu positif dan negatif. Kemudian dilakukan pemodelan dengan metode pembobotan Unigram, Bigram, Trigram, N-Gram (1-2) Dan N-Gram (1-3) menggunakan Algoritme Naïve Bayes pada Aplikasi Weka. Pembuatan model menggunakan dataset yang berjumlah 646 kalimat. Hasil tertinggi yang diperoleh pada penelitian ini adalah dengan menggunakan Pembobotan Unigram, yaitu : akurasi 81,4%, presisi 81,5 % , recall 81,3 % dengan catatan waktu 0,3s.

Kata kunci: klasifikasi, naïve bayes, pilpres 2019, twitter, unigram.


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DOI: https://doi.org/10.33330/jurteksi.v7i1.851

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