Simulasi Perhitungan Premi Asuransi Kesehatan dan Jiwa pada Penderita Covid-19 yang Dipengaruhi Model Penyebaran Penyakit Menular SIDRS
Kata Kunci:
Model SIDRS, aktuaria, manfaat, premi, COVID-19Abstrak
Determination of health and death insurance benefits according to the needs of policyholders is very important to determine from the beginning of making an insurance policy, especially for insurance that takes over the risk of being infected with the COVID-19 virus. Several factors that must be taken into account in determining the amount of benefits and premiums due to COVID-19 are the human population factor that is susceptible, infected and death in the SIDRS infectious disease spread model. In this study, the influence of these three factors on actuarial calculations is examined in more depth to produce an appropriate premium determination formula by taking into account two payment schemes in lump sum and annuity. From the simulation results by applying data on COVID-19 cases in Indonesia to determine the parameters of the SIDRS model, it is concluded that the premium with an annuity benefit payment scheme is smaller than the premium with a lump sum benefit scheme. Furthermore, it is also concluded that if the population of policyholders increases, the premium price will also be lower.
Referensi
N. Brouhns, M. Denuit, and J. K. Vermunt, “Measuring the longevity risk in mortality projections,” Bulletin of the Swiss Association of Actuaries, vol. 2, no. 1, pp. 105–130, 2002.
B. Benjamin and A. S. Soliman, “Mortality on the move. Institute of actuaries.” Oxford, 1993.
A. S. Macdonald, The second actuarial study of mortality in Europe. Institute of Actuaries, 1997.
A. S. McDonald, A. J. C. Cairns, P. L. Gwilt, and K. A. Miller, “An international comparison of recent trends in mortality,” British Actuarial Journal, vol. 4, pp. 3–141, 1998.
A. Olivieri, “Uncertainty in mortality projections: an actuarial perspective,” Insur Math Econ, vol. 29, no. 2, pp. 231–245, 2001.
F. Janssen, “Advances in mortality forecasting: introduction,” Genus, vol. 74, no. 1. SpringerOpen, pp. 1–12, 2018.
Y. Gao, H. L. Shang, and Y. Yang, “High-dimensional functional time series forecasting: An application to age-specific mortality rates,” J Multivar Anal, vol. 170, pp. 232–243, 2019.
R. Giacometti, M. Bertocchi, S. T. Rachev, and F. J. Fabozzi, “A comparison of the Lee–Carter model and AR–ARCH model for forecasting mortality rates,” Insur Math Econ, vol. 50, no. 1, pp. 85–93, 2012.
I. Nursaadah, E. Puspita, and R. Marwati, “Metode Peramalan Mortalita Menggunakan Metode Lee-Carter,” Jurnal EurekaMatika, vol. 3, no. 1, pp. 17–30, 2015.
R. Unaeni, N. Satyahadewi, and H. Perdana, “PERAMALAN TINGKAT KEMATIAN (MORTALITA),” Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, vol. 6, no. 01.
K. D. Jayanti et al., “Proyeksi Angka Kematian Bayi di Rumah Sakit X Kabupaten Kediri dengan Single Exponential Smoothing,” Jurnal Berkala Kesehatan, vol. 6, no. 2, pp. 50–54, 2020.
A. Aryati, I. Purnamasari, and Y. N. Nasution, “Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing,” EKSPONENSIAL, vol. 11, no. 1, pp. 99–106, 2021.
C. V. Hudiyanti, F. A. Bachtiar, and B. D. Setiawan, “Perbandingan Double Moving Average dan Double Exponential Smoothing untuk Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Bandara Ngurah Rai,” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, vol. 2548, p. 964X, 2019.
R. Rachman, “Penerapan metode moving average dan exponential smoothing pada peramalan produksi industri garment,” Jurnal Informatika, vol. 5, no. 2, pp. 211–220, 2018.
D. C. M. Dickson, M. R. Hardy, and H. R. Waters, “Actuarial Mathematics for Life Contingent Risks.,” Annals of Actuarial Science, vol. 4, no. 2, p. 339, 2009.
P. Subagyo, “Forecasting, Konsep dan Aplikasi, Yogyakarta,” Edisi Keempat, BPFE UGM, 2002.
E. Herjanto, “Manajemen Produksi dan Operasi Cetakan Ketiga,” PT. Grasindo, Jakarta, 2013.
S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting methods and applications. John wiley & sons, 2008.
J. H. Barus and R. Ramli, “Analisis peramalan ekspor Indonesia pasca krisis keuangan eropa dan Global tahun 2008 dengan metode dekomposisi,” Jurnal Ekonomi dan Keuangan, vol. 1, no. 3, p. 14880, 2013.
D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to time series analysis and forecasting. John Wiley & Sons, 2015.
