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. 

References

A. Bastian, H. Sujadi, and G. Febrianto, “Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka),†no. 1, pp. 26–32.

Abdullah, D., Susilo, S., Ahmar, A. S., Rusli, R., & Hidayat, R. (2021). The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data. Quality & Quantity, 1-9.

Ahmar, A. S., Napitupulu, D., Rahim, R., Hidayat, R., Sonatha, Y., & Azmi, M. (2018, June). Using K-Means Clustering to Cluster Provinces in Indonesia. In Journal of Physics: Conference Series (Vol. 1028, No. 1, p. 012006). IOP Publishing.

Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.

A. S. Jati, “Jumlah pengguna twitter meningkat†04 Mei, 2020.

A. Sani, “Penerapan Metode K-Means Clustering Pada Perusahaan,†J. Ilm. Teknol. Inf., no. 353, pp. 1–7, 2018.

D. H. Rarasati, “Dampak Kenaikan Tarif Bpjs Kesehatan terhadap pelayanan Kesehatan di Kota Malang,†J. Polit. Muda, vol. 6, no. 1, pp. 34–40, 2017.

D. C. Manning, P. Raghavan, and H. Schutze, An Introduction to Information Retrieval. 2009.

Elisawati, D. Wahyuni, and A. Arianto, “Analisa Clustering Pada Data Pelanggaran Lalulintas Di Pengadilan Negeri Dumai Dengan Menggunakan Metode K-Means,†JISKA (Jurnal Inform. Sunan Kalijaga), vol. 3, no. 3, pp. 50–61, 2019.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,†J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019.

H. Siqueira and F. Barros, “A Feature Extraction Process for Sentiment Analysis of Opinions on Services,†Proc. III Int. Work. Web Text Intell., 2010.

Kuswandi, D., Surahman, E., Thaariq, Z. Z. A., & Muthmainnah, M. (2018, October). K-Means clustering of student perceptions on project-based learning model application. In 2018 4th International Conference on Education and Technology (ICET) (pp. 9-12). IEEE.

Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018, December). Customer segmentation using K-means clustering. In 2018 international conference on computational techniques, electronics and mechanical systems (CTEMS) (pp. 135-139). IEEE.

M. Habibi and P. W. Cahyo, “Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform,†IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, p. 399, 2019.

N. I. Febianto and N. Palasara, “Analisa Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018,†J. Sisfokom (Sistem Inf. dan Komputer), vol. 8, no. 2, p. 130, 2019.

N. Putu, E. Merliana, and A. J. Santoso, “Analisa Penentuan Jumlah Cluster Terbaik pada Metode K-Means,†pp. 978–979.

P. Studi et al., “PENERAPAN ALGORITMA K-MEANS CLUSTER ING SEBAGAI STRATEGI PROMOSI PENERIMAAN MAHASISWA BARU PADA UNIVERSITAS HASYIM ASY ’ ARI JOMBANG Aries Dwi Indriyanti Indana Lazulfa,†vol. 04, pp. 20–27, 2020.

P. W. Cahyo, P. Studi, T. Informatika, U. Jenderal, and A. Yani, “KLASTERISASI TIPE PEMBELAJAR SEBAGAI PARAMETER Abstrak Data Collection,†pp. 49–55, 2018.

U. A. Nasron and M. Habibi, “Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means,†Compiler, vol. 9, no. 1, pp. 51–61, 2020.

Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE access, 8, 80716-80727.

Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018, April). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP conference series: materials science and engineering (Vol. 336, No. 1, p. 012017). IOP Publishing.

Yuan, C., & Yang, H. (2019). Research on K-value selection method of K-means clustering algorithm. J, 2(2), 226-235.

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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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