Sentiment Analysis in Social Media: Case Study in Indonesia

Authors

  • Amri Muhaimin Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Tresna Maulana Fahrudin Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Syifa Syarifah Alamiyah Faculty of Social and Political Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Heidy Arviani Faculty of Social and Political Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Ade Kusuma Faculty of Social and Political Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Allan Ruhui Fatmah Sari Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
  • Angela Lisanthoni Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia

DOI:

https://doi.org/10.11594/nstp.2024.4106

Keywords:

Stunting, review, latent Dirichlet allocation

Abstract

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.

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Published

14-05-2024

Conference Proceedings Volume

Section

Articles

How to Cite

Muhaimin, A., Fahrudin, T. M. ., Alamiyah, S. S. ., Arviani, H. ., Kusuma, A. ., Sari, A. R. F. ., & Lisanthoni, A. . (2024). Sentiment Analysis in Social Media: Case Study in Indonesia. Nusantara Science and Technology Proceedings, 2024(41), 27-30. https://doi.org/10.11594/nstp.2024.4106

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