Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

Authors

  • Rani Puspita Bina Nusantara University
  • Agus Widodo Bina Nusantara University

DOI:

https://doi.org/10.32493/informatika.v5i4.7622

Keywords:

Sentiment Analysis, BPJS, Twitter, Data Mining

Abstract

BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.

Author Biographies

Rani Puspita, Bina Nusantara University

Computer Science Department

Agus Widodo, Bina Nusantara University

Computer Science Department

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Published

2020-12-31

Issue

Vol. 5 No. 4 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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Article

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