@inproceedings{7181bfa23cca4e5fb908cc33860638ed,
title = "Analysis the Issue of Increasing National Health Insurance (BPJS Kesehatan) Rates through Community Perspectives on Social Media: A Case Study of Drone Emprit",
abstract = "Mobile phones are devices that always embedded in the society of Indonesian people, around 91% of Indonesians population use mobile phones and around 60% are smartphones. The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. During that time period 360,820 total conversations occurred with a percentage of sentiment of 91% showing negative sentiment, 5% positive and 4% neutral. This data shows that the majority of Indonesian people do not agree with the increase in fees of national health insurances (BPJS Kesehatan).",
keywords = "big data, drone emprit, national health insurance, social media, social network analysis, text mining",
author = "Laagu, {Muh Asnoer} and {Setyo Arifin}, Ajib",
note = "Funding Information: Blue nodes are accounts that are involved in social media talks, while red links are networks that connect each node to other nodes. Red link shows that the network has negative sentiment, while the node with green link shows positive sentiment. In Figure 13 above the link is dominated by red colored tissue, while the green colored network is barely visible. There are 3 clusters that talk about issues of increasing BPJS Kesehatan fees. First cluster are tweeting that talk about the issue of rising BPJS related to political issues, second cluster are media that get a lot engagement and third is a professional cluster who talk about providing solutions from a professional perspective about issue of increasing BPJS Kesehatan fees V. CONCLUSION The issue of BPJS Kesehatan increases rates has captured a lot of public attention, especially on social media platforms. Twitter is the most social media platform that talks about the issue. Discussion on the issue of BPJS Kesehatan increases rates reached its peak on October 8, 2019 when the government wanted to set an increase in BPJS contribution rates for all classes up to 100%. Drone Emprit captures twitter data for 2 months, around 360,821 conversations on social media. both in the form of retweets and mention-replies related to the issue of increasing BPJS contributions on social media. Around 91% of the total conversation showed negative sentiment towards the issue, the remaining 5% had positive sentiment and the other 4% were in a neutral position. The amount of negative sentiment on the issue of increasing BPJS fees shows that the majority of people disagree with the increase in BPJS Kesehatan increased fees. Results of this analysis are expected to be able to encourage the Government to be able to make a public policy to canceled BPJS Kesehatan increased fees but allocate another budget to cover the National Health Insurance deficit. Social media that is captured is Twitter, in the future social media data mining can include several social media platforms such as Instagram, Facebook, Linkedin, Youtube, and even other online media. Twitter users are not as many as Facebook and Instagram users. Result in this paper only provide an overview of the conversations that occur on the Twitter social media platform ACKNOWLEDGMENT Thank you to Drone Emprit Academic who has been the main source in this writing analysis. This research is funded by PITTA Universitas Indonesia grant 2019 with contract number: NKB-0698/UN2.R3.1/HKP.05.00/2019. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Smart Technology and Applications, ICoSTA 2020 ; Conference date: 20-02-2020",
year = "2020",
month = feb,
doi = "10.1109/ICoSTA48221.2020.1570615599",
language = "English",
series = "Proceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceeding - ICoSTA 2020",
address = "United States",
}
