Cluster analysis of province case mix index on Indonesian national health security (BPJS Kesehatan) system


Raditya Prabaswara
Muhammad Ikhsan Kalla
Penbi Opmel Siregar
Muhammad Mizmara Alan Falihin
Benjamin Saut Parulian Simanjuntak
Samuel Sormin


Disease case is a diverse and complex problem which faced by health insurance institutions. BPJS Kesehatan, Indonesian national health security, has undoubtedly faced this problem, especially because of the socioeconomic gap between 34 provinces in Indonesia. To describe the diverse and complex conditions of resource needs for all hospital patients, this paper will calculate the Case Mix Index (CMI) for each province and conduct a cluster analysis to group provinces based on the CMI and total health facility visits similarity. Once the CMI for each province is identified, the cluster analysis is executed by using K-Means and Hierarchical clustering method to compare each result. The first step of cluster analysis is to identify the optimal number of clusters. In this article, several K-value selection techniques is used to find out the optimal number of clusters. By using K-value selection methods, the optimal number of clusters is two province clusters. The first cluster, namely Cluster 1, consists of four provinces which are DKI Jakarta, Jawa Barat, Jawa Tengah, and Jawa Timur. The second cluster, denoted as Cluster 2, consists of the rest provinces which are not included in Cluster 1. Although the optimal number of clusters are identified, this paper also adjust the cluster analysis result to provide comparison with the current INACBG regionalization. The result of this paper can be utilized as a recommendation for INA-CBGS tariff regionalization since using CMI as one of the clustering variables could depict the diverse condition in 34 provinces in Indonesia.


How to Cite
Prabaswara, R., Kalla, M. I., Siregar, P. O., Falihin, M. M. A., Simanjuntak, B. S. P. and Sormin, S. (2024) “Cluster analysis of province case mix index on Indonesian national health security (BPJS Kesehatan) system”, Science Midwifery, 12(2), pp. 713-721. doi: 10.35335/midwifery.v12i2.1536.


Abdullah, D., Susilo, S., Ahmar, A. S., Rusli, R., & Hidayat, R. (2022). 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 and Quantity, 56(3).
Agustina, R., Dartanto, T., Sitompul, R., Susiloretni, K. A., Suparmi, Achadi, E. L., Taher, A., Wirawan, F., Sungkar, S., Sudarmono, P., Shankar, A. H., Thabrany, H., Susiloretni, K. A., Soewondo, P., Ahmad, S. A., Kurniawan, M., Hidayat, B., Pardede, D., Mundiharno, … Khusun, H. (2019). Universal health coverage in Indonesia: concept, progress, and challenges. In The Lancet (Vol. 393, Issue 10166).
Aldino, A. A., Darwis, D., Prastowo, A. T., & Sujana, C. (2021). Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency. Journal of Physics: Conference Series, 1751(1).
Chang, J., & Zhang, L. (2019). Case Mix Index weighted multi-objective optimization of inpatient bed allocation in general hospital. Journal of Combinatorial Optimization, 37(1).
Delamater, P. L., Shortridge, A. M., & Messina, J. P. (2013). Regional health care planning: A methodology to cluster facilities using community utilization patterns. BMC Health Services Research, 13(1).
Gunawan, J., & Aungsuroch, Y. (2015). Indonesia health care system and Asean economic community. International Journal of Research in Medical Sciences.
Han, B., Chen, X., & Li, Q. (2018). Application of case mix index in the allocation of nursing human resources. Journal of Nursing Management, 26(6).
Himawan, N. S. S. (2024). Literature Review: Analysis of Hospital Readiness in Facing Standard Inpatient Class (KRIS). Pharmacology, Medical Reports, Orthopedic, and Illness Details, 3(1), 7–19.
Mahendradhata, Y., Trisnantoro, L., Listyadewi, S., Soewondo, P., Marthias, T., Harimurti, P., & Prawira, J. (2017). The Republic of Indonesia health system review. Health Systems in Transition, 7(1).
Mendez, C. M., Harrington, D. W., Christenson, P., & Spellberg, B. (2014). Impact of hospital variables on case mix index as a marker of disease severity. Population Health Management, 17(1).
Nurwahyuni, A., & Setiawan, E. (2020). Kinerja Rumah Sakit Swasta dengan Pembayaran INA-CBGs di Era Jaminan Kesehatan Nasional: Casemix, Casemix Index, Hospital Base Rate. Jurnal Ekonomi Kesehatan Indonesia, 4(2).
Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1).
Ramadhan, L., Aritonang, M., & Anggriani, Y. (2022). Analisis Perbedaan Tarif Rumah Sakit dan Tarif INA-CBGs Pelayanan Rawat Jalan di RSUD Pasar Rebo Jakarta. Journal of Islamic Pharmacy, 6(2).
Roesbiantoro, A., Setianto, B., Adriansyah, A. A., Asih, A. Y. P., Setiyowati, E., Bistara, D. N., & Sa’adah, N. (2022). Description Of Characteristics, Diagnosis And Financing Of BPJS Patients In ENT Poly Health Service Facility Level 2. Medical Technology and Public Health Journal, 6(2), 169–176.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(C).
Schubert, E. (2023). Stop using the elbow criterion for k-means and how to choose the number of clusters instead. ACM SIGKDD Explorations Newsletter, 25(1).
Setiawan, E., Witjaksana, B., & TJendani, H. T. (2023). Analysis of Risk Management in Building Workers of SMAN 5 Brawijaya Building Kediri. International Journal on Advanced Technology, Engineering, and Information System, 2(4), 334–346.
Triyudawati, W., Kustriyani, A., & Prasasti, A. (2023). Analysis of Hospital and INA-CBG’S Rates of JKN Outpatient in Genteng Regional Public Hospital in 2021. Proceedings of International Pharmacy Ulul Albab Conference and Seminar (PLANAR), 3, 22–27.
Wang, D., & Gao, Q. (2017). Efficiency assessment and resource allocation for hospitals by data envelopment analysis. 2017 3rd International Conference on Information Management, ICIM 2017.
Wu, J. (2012). Advances in K-means Clustering: a data mining thinking. In Springer Theses: recognizing outstanding Ph.D. Research.
Xie, H., Cui, X., Ying, X., Hu, X., Xuan, J., & Xu, S. (2022). Development of a novel hospital payment system – Big data diagnosis & intervention Packet. Health Policy OPEN, 3.
Yuan, C., & Yang, H. (2019). Research on K-Value Selection Method of K-Means Clustering Algorithm. J, 2(2).