Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier

Authors

  • Neni Sari Putri Juana Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

DOI:

https://doi.org/10.30865/json.v4i4.6168

Keywords:

President, Twitter, Sentiment Analysis, Naïve Bayes Classifier

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.

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Published

2023-07-02

How to Cite

Juana, N. S. P., Haerani, E., Syafria, F., & Budianita, E. (2023). Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier. Jurnal Sistem Komputer Dan Informatika (JSON), 4(4), 570–576. https://doi.org/10.30865/json.v4i4.6168

Issue

Vol. 4 No. 4 (2023): Juni 2023

Section

Articles

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