PENGGEROMBOLAN TWEET BADAN NASIONAL PENANGGULANGAN BENCANA INDONESIA PERIODE AGUSTUS 2018 FEBRUARI 2019 MENGGUNAKAN TEXT MINING

Authors

  • Windyana Pusparani Department of Statistics, IPB University, Indonesia
  • Agus M Soleh Department of Statistics, IPB University, Indonesia
  • Akbar Rizki Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v4i4.525

Keywords:

clustering analysis, disaster, k-means, text mining

Abstract

Twitter is a popular social media platform for communicating between its users by writing short messages in limited characters, called tweets. Extracting data information that has non-structured form and huge-sized, usually known as text mining. Badan Nasional Penanggulangan Bencana Indonesia (@BNPB_Indonesia) is the official twitter account of the government agency in the field of disaster management that uses twitter to share much information about disasters that have occurred in Indonesia. This study aims to determine the characteristics of all tweets and to group the types of tweets that they shared based on the similarity of its content. The data used in the study came from BNPB Indonesia's tweets with the period of taking tweets 6th of August 2018 to 16th of February 2019. The cluster result obtained by the k-Means method was 4 groups. The characteristics of the first cluster contained information about the weather conditions in Yogyakarta, the second cluster was about the source and magnitude of an earthquake, and the third group was about the occurrence of earthquakes in Lombok. However, the fourth group characteristic couldn’t be specifically identified because there was no clear distinction between other tweets in its members.

Downloads

Download data is not yet available.

References

Adriani, M., Asian, J., Nazief, B., Tahaghoghi, S. M., & Williams, H. E. (2007). Stemming indonesian: A confix-stripping approach. ACM Transactions on Asian Language Information Processing (TALIP), 6(4): 1-33.

Chen, X., Vorvoreanu, M., & Madhavan, K. (2014). Mining social media data for understanding students' learning experiences. IEEE Transactions on learning technologies, 7(3): 246-259.

Feldman, R., Sanger, J., et al. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press.

Mattjik, A. A. & Sumertajaya, I. (2011). Sidik peubah ganda dengan menggunakan SAS. Bogor (ID): IPB Press.

Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5): 513-523.

Slamet, C., Rahman, A., Ramdhani, M. A., & Darmalaksana, W. (2016). Clustering the verses of the holy qur'an using k-means algorithm. Asian Journal of Information Technology, 15(24): 5159-5162.

Tala, F. (2003). A study of stemming effects on information retrieval in Bahasa Indonesia. Amsterdam (NL): Institute for Logic, Language and Computation, Universiteit van Amsterdam.

Downloads

Published

2020-12-25

How to Cite

Pusparani, W., Soleh, A. M., & Rizki, A. (2020). PENGGEROMBOLAN TWEET BADAN NASIONAL PENANGGULANGAN BENCANA INDONESIA PERIODE AGUSTUS 2018 FEBRUARI 2019 MENGGUNAKAN TEXT MINING. Indonesian Journal of Statistics and Its Applications, 4(4), 590–603. https://doi.org/10.29244/ijsa.v4i4.525

Issue

Section

Articles