Application of ANN-GARCH on Volatility Analysis of Forecasting the Level of First Level Hospitalization Cost Claims (RITP) BPJS Health

Andi Daniah Pahrany; Melani Nur Wakhidah; Siti Mariam Binti Norrulashikin

doi:10.30812/varian.v8i3.5422

Authors

Andi Daniah Pahrany Universitas Negeri Malang, Malang, Indonesia
Melani Nur Wakhidah Universitas Negeri Malang, Malang, Indonesia
Siti Mariam Binti Norrulashikin Universitas Teknologi Malaysia, Johor Bahru, Malaysia

DOI:

https://doi.org/10.30812/varian.v8i3.5422

Keywords:

Artificial Neural Network, First-Level Inpatient Care, Generalized Autoregressive Conditional Heteroscedasticity, Volatility

Abstract

The volatility of First-Level Inpatient Care (RITP) claim costs poses a substantial challenge to BPJS Health’s financial management, underscoring the need for accurate forecasting methods. This study employs Artificial Neural Network and Generalized Autoregressive Conditional Heteroscedasticity models to examine volatility dynamics and assess predictive performance. The results indicate that both models capture nonlinear patterns, heteroskedasticity, and temporal dependencies, with evidence that past fluctuations largely influence current volatility. Forecast accuracy is generally high, as reflected in the small discrepancies between predicted and actual values across most provinces. Nevertheless, the models exhibit limitations in capturing extreme peaks and troughs, where abrupt claim variations are not fully represented. These findings highlight the effectiveness of Artificial Neural Networks and Generalized Autoregressive Conditional Heteroscedasticity in modeling claim volatility, while emphasizing the need for model refinement, such as parameter optimization or integration with complementary approaches, to enhance forecasting reliability.

Downloads

Download data is not yet available.

References

Ali, F., Suri, P., Kaur, T., & Bisht, D. (2022). Modelling time-varying volatility using GARCH models: Evidence from the Indian stock market. F1000Research, 11, 1098. https://doi.org/10.12688/f1000research.124998.2

Beck, M. W. (2018). NeuralNetTools : Visualization and Analysis Tools for Neural Networks. Journal of Statistical Software, 85(11). https://doi.org/10.18637/jss.v085.i11

Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer International Publishing. https://doi.org/10.1007/978-3-319-29854-2

Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022

Here is the cleaned list for your latest set of citations. I have removed the line breaks to ensure each entry is formatted as a single continuous paragraph, suitable for academic use.

Jauhari, D., Himawan, A., & Dewi, C. (2016). Prediksi Distribusi Air PDAM Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Di PDAM Kota Malang. Jurnal Teknologi Informasi dan Ilmu Komputer, 3(2), 83. https://doi.org/10.25126/jtiik.201632155

Kanal, F. A., Manurung, T., & Prang, J. D. (2018). Penerapan Model GARCH (Generalized Autoregressive Conditional Heteroscedasticity) dalam Menghitung Nilai Beta Saham Indeks PEFINDO25. Jurnal Ilmiah Sains, 18(2), 67. https://doi.org/10.35799/jis.18.2.2018.19732

Kong, J., & Lund, R. (2023). Seasonal count time series. Journal of Time Series Analysis, 44(1), 93–124. https://doi.org/10.1111/jtsa.12651

Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet, 15(8), 255. https://doi.org/10.3390/fi15080255

Kurniasari, D., Mukhlisin, Z., Wamiliana, W., & Warsono, W. (2023). Performance of the Accuracy of Forecasting the Consumer Price Index Using the GARCH and ANN Methods. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 17(2), 0931–0944. https://doi.org/10.30598/barekengvol17iss2pp0931-0944

Maitra, S., & Politis, D. N. (2024). Prepivoted Augmented Dickey-Fuller Test with Bootstrap-Assisted Lag Length Selection. Stats, 7(4), 1226–1244. https://doi.org/10.3390/stats7040072

Makridakis, S., Hyndman, R. J., & Petropoulos, F. (2020). Forecasting in social settings: The state of the art. International Journal of Forecasting, 36(1), 15–28. https://doi.org/10.1016/j.ijforecast.2019.05.011

Mayatopani, H. (2021). Implementation of ANN and GARCH for Stock Price Forecasting. Journal of Applied Data Sciences, 2(4), 109–134. https://doi.org/10.47738/jads.v2i4.41

Monti, G. S., Mateu Figueras, G., Ortego Martínez, M. I., Pawlowsky Glahn, V., & Egozcue Rubí, J. J. (2017). Modified Kolmogorov-Smirnov test of goodness of fit, 152–158. https://hdl.handle.net/2117/105422

Naik, N., & Mohan, B. R. (2021). Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market. Mathematics, 9(14), 1595. https://doi.org/10.3390/math9141595

Perwitasari, A., & Atikah, N. (2020). Model Fungsi Transfer Multi Input dalam Peramalan Penerimaan Pajak Hotel Kota Malang Tahun 2019. Jurnal Kajian Matematika dan Aplikasinya (JKMA), 1(1), 1–9. https://doi.org/10.17977/um055v1i12020p1-9

Petkov, P., Shopova, M., Varbanov, T., Ovchinnikov, E., & Lalev, A. (2024). Econometric Analysis of SOFIX Index with GARCH Models. Journal of Risk and Financial Management, 17(8), 346. https://doi.org/10.3390/jrfm17080346

Rahmawati, N., & Lestari, T. E. (2019). Implementasi Model Fungsi Transfer dan Neural Network untuk Meramalkan Harga Penutupan Saham (Close Price). Jurnal Matematika, 9(1), 11–25. https://doi.org/10.24843/JMAT.2019.v09.i01.p107

Sako, K., Mpinda, B. N., & Rodrigues, P. C. (2022). Neural Networks for Financial Time Series Forecasting. Entropy, 24(5), 657. https://doi.org/10.3390/e24050657

Sari, H. R., Wahyuningsih, S., & Siringoringo, M. (2024). Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0). EKSPONENSIAL, 15(1), 1–10. https://doi.org/10.30872/eksponensial.v15i1.1265

Sekhar, C., & Meghana, P. S. (2020). A Study on Backpropagation in Artificial Neural Networks. Asia-Pacific Journal of Neural Networks and Its Applications, 4(1), 21–28. https://doi.org/10.21742/AJNNIA.2020.4.1.03

Setiawan, B., Ben Abdallah, M., Fekete-Farkas, M., Nathan, R. J., & Zeman, Z. (2021). GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy. Journal of Risk and Financial Management, 14(12), 576. https://doi.org/10.3390/jrfm14120576

Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples. Springer International Publishing. https://doi.org/10.1007/978-3-319-52452-8

Susanti, D., Maraya, N. S., Sukono, S., & Saputra, J. (2024). Prediction of Motor Vehicle Insurance Claims Using ARIMA-GARCH Models. Operations Research: International Conference Series, 5(3), 86–92. https://doi.org/10.47194/orics.v5i3.331

Susila, M. R., Jamil, M., & Santoso, B. H. (2023). Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia. Jambura Journal of Mathematics, 5(1), 46–58. https://doi.org/10.34312/jjom.v5i1.14889

Svalova, A., Walshaw, D., Lee, C., Demyanov, V., Parker, N. G., Povey, M. J., & Abbott, G. D. (2021). Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference. Scientific Reports, 11(1), 6698. https://doi.org/10.1038/s41598-021-85926-8

Yuwanita, N., Adi, S., Mawarni, D., & Wardani, H. E. (2022). Analisis Faktor-Faktor Perilaku Pembayaran Iuran Program Jaminan Kesehatan Nasional Peserta Mandiri di Kecamatan Klojen Kota Malang. Sport Science and Health, 4(12), 1059–1069. https://doi.org/10.17977/um062v4i122022p1059-1069