Analysis and visualization of BPJS on twitter using K-means clustering

Authors

  • Andika Bayu Saputra Universitas Jenderal Achmad Yani Yogyakarta, Indonesia https://orcid.org/0000-0002-6754-3080
  • Puji Winar Cahyo Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
  • Muhammad Habibi Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
  • Adri Priadana Universitas Jenderal Achmad Yani Yogyakarta, Indonesia

DOI:

https://doi.org/10.31101/ijhst.v3i3.2466

Keywords:

BPJS, health, K-means, clustering

Abstract

Social security agency (BPJS) Health exists to provide national social security to meet the basic needs appropriate for all levels of society based on the principle of humanity. Originated from a change in the contribution premium policy, it is demanded by the organizers and health service providers to be able to provide safe, quality, affordable health facilities. But unfortunately, the government's efforts in realizing public welfare, especially in the field of health, are not fully supported by the community because of the ever- changing premium contribution policy and the health services they receive. The latest information developments related to BPJS on social media that can be easily accessed by the public. One of them is by using the Twitter platform as a place to exchange information using hashtags. The hashtag data can be processed and obtained information to be used as a tool for decision making. This study aims to analyze and visualize BPJS data on the Twitter platform using the K-Means clustering method. K-Means clustering method is a method of clustering data mining using the descriptive model concept. K- means method can use to explain the algorithm in determining an object into a specific cluster based on the nearest average. 

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Published

2022-06-07

How to Cite

Saputra, A. B., Cahyo, P. W., Habibi, M., & Priadana, A. (2022). Analysis and visualization of BPJS on twitter using K-means clustering. International Journal of Health Science and Technology, 3(3). https://doi.org/10.31101/ijhst.v3i3.2466

Issue

Vol. 3 No. 3 (2022): April

Section

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