Twitter Data Classification using Multinomial Naive Bayes for Tropical Diseases Mapping in Indonesia

Authors

  • Romy Ranovan Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.
  • Afrizal Doewes Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.
  • Ristu Saptono Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

Keywords:

Classification, Mapping, Multinomial Naive Bayes, Tropical Diseases,

Abstract

Tropical diseases are diseases commonly found in tropical and sub-tropical regions. The goal of this research is to map the tropical diseases based on data from Twitter to help policymakers take essential steps regarding health condition in Indonesia. Tweets classification was conducted in two phases, both using Multinomial Naive Bayes. The first phase is to filter non-Indonesian tweets, and the second phase is to classify the tweets containing diseases information. The result shows the type of the diseases and location with high accuracy supported by map visualization.

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Published

2018-07-03

How to Cite

Ranovan, R., Doewes, A., & Saptono, R. (2018). Twitter Data Classification using Multinomial Naive Bayes for Tropical Diseases Mapping in Indonesia. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 155–159. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4335

Issue

Vol. 10 No. 2-4: Enhancing Digital Trust for Sustainable Development

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)