Analisis Sentimen Komentar Pengguna Youtube terhadap  Kebijakan Baru Badan Penyelenggara Jaminan Kesehatan Sosial Menggunakan Naïve Bayes

Muhamad Taufik Sugandi; Martanto Martanto; Umi Hayati

doi:10.36499/jinrpl.v6i1.10301

Analisis Sentimen Komentar Pengguna Youtube terhadap Kebijakan Baru Badan Penyelenggara Jaminan Kesehatan Sosial Menggunakan Naïve Bayes

Muhamad Taufik Sugandi, Martanto Martanto, Umi Hayati

Abstract

Many social media platforms are used by the public to express opinions and seek information. YouTube is a media sharing site, a kind of virtual entertainment for sharing video and audio media. YouTube has become one of the most popular video viewing platforms today. There are various topics discussed in YouTube videos, one of which is the discussion about the new policy of removing class 1, 2, and 3 systems and replacing them with the Standard Inpatient Class (KRIS) system in the Social Security Administrator (BPJS) for Health. Health is also a very important issue and is still a topic that is frequently discussed everywhere and anytime. BPJS for Health greatly helps the public in overcoming the declining economy, with the existence of BPJS for Health the public does not need to pay for medical expenses. Therefore, sentiment analysis will be conducted on the services provided by BPJS for Health to determine whether public opinion about BPJS is positive, neutral, or negative. The algorithm used is Naïve Bayes. In this sentiment analysis, 2,968 datasets were crawled from YouTube using several keywords related to BPJS for Health. Based on the research results using the Naïve Bayes algorithm, the highest accuracy of the model on the test data reached 96% with a ratio of 80:20. This indicates that the model is capable of classifying sentiment in comments well. This study is dominated by positive sentiment comments at 45.9% or 1,354 data out of a total of 2,948 comment data, indicating strong support for the new policy and many who are very helped by the services of BPJS for Health.

Keywords

Sentiment Analysis, BPJS for Health, KRIS, Naïve Bayes, YouTube

Full Text:

PDF

References

Dwianto, E., & Sadikin, M. (2021). Analisis Sentimen Transportasi Online pada Twitter Menggunakan Metode Klasifikasi Naïve Bayes dan Support Vector Machine. Format : Jurnal Ilmiah Teknik Informatika, 10(1). https://doi.org/10.22441/format.2021.v10.i1.009

Ginabila, G., & Fauzi, A. (2023). Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine. Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika, 6(2). https://doi.org/10.47324/ilkominfo.v6i2.180

Muhamad, H., Prasojo, C. A., Sugianto, N. A., Surtiningsih, L., Cholissodin, I., Ilmu, F., Universitas, K., & Optimization, P. S. (2017). Optimasi Naïve Bayes Classifier Dengan Menggunakan Particle. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 4(3), 180–184.

Muzaki, A., & Witanti, A. (2021). Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-19 By Method Naive Bayes Classifier. Jurnal Teknik Informatika (Jutif), 2(2), 101–107. https://doi.org/10.20884/1.jutif.2021.2.2.51

DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10301