Optimalisasi Prediksi Klaim Kendaraan Bermotor Menggunakan Time Series Analysis dengan Pendekatan Variasi Kalender dalam Perusahaan Asuransi Umum
DOI:
https://doi.org/10.46880/jmika.Vol10No1.pp1-6Keywords:
ARIMA, Calendar Variation, Claim Prediction, Motor Vehicle Insurance, Time Series AnalysisAbstract
General insurance companies face significant challenges in predicting motor vehicle insurance claims due to seasonal fluctuations and calendar variations. This study aims to optimize the accuracy of claim predictions using the Autoregressive Integrated Moving Average (ARIMA) method combined with a calendar variation approach. Data analysed includes nominal claim values from January 2015 to December 2023. Regression analysis results show that calendar variations, specifically periods "before" and "during" the Eid al-Fitr holiday, significantly impact claim reductions. The best-fit time series model obtained is ARIMA (1,0,1)(0,1,1)4. This model satisfies white noise and normality assumptions for residuals, providing a robust framework for data-driven decision-making, reserve allocation, and operational readines.
References
Adelia, M., Arsyadona, A., Tafana, A., Marwah, S., & Andika, B. (2024). Menimbang Efektivitas Asuransi Sebagai Instrumen Perlindungan Finansial “Apakah Risiko Dan Manfaat Seimbang?” Jurnal Akademik Ekonomi Dan Manajemen, 1(4), 394–405.
Bell, W. R., & Hillmer, S. C. (1983). Modeling Time Series with Calendar Variation. Journal of the American Statistical Association, 78(383), 526–534. https://doi.org/10.1080/01621459.1983.10478005
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
Gempati, A., Faisal Agymnastiar Rahmad Fradani, Rayya Malik Ibrahim, Tenry Kusuma Astuti, & Yusuf Riyan Prasetyo. (2025). Peramalan Data IHSG 2021-2025 di Indonesia Dengan Time Series Modeling Autoregressive Integrated Moving Average (ARIMA). Jurnal Ilmiah Ekonomi Dan Manajemen, 3(5), 225–234. https://doi.org/10.61722/jiem.v3i5.4650
Junaedi, L., Damastuti, N., Latipah, L., & Widodo, A. (2025). Penerapan Metode Seasonal ARIMA (SARIMA) untuk Peramalan Penjualan Barang dengan Pola Musiman Tahunan. JISEM (Jurnal Informatika, Sistem Informasi, Dan Elektro Modern), 1(1), 38–48.
Pradana, B. L. (2025). Time Series Forecasting of LQ45 Stock Index Using ARIMA: Insights and Implications. Review of Management, Accounting and Tourism Studies, 1(1), 27–40.
Purwantoro, A. K. P., Nadia, A. A., Anggraeni, D., Alamsyah, N. E. A., & Ramadhan, Y. (2025). Analisis Metode Springate Dalam Memprediksi Kebangkrutan Pada Perusahaan Asuransi Yang Terdaftar di BEI. Kompak :Jurnal Ilmiah Komputerisasi Akuntansi, 18(2), 446–453. https://doi.org/10.51903/kompak.v18i2.2901
Republik Indonesia. (2014). Undang-Undang Republik Indonesia Nomor 40 Tahun 2014.
Yakin, Y. A. (2021). Peran Asuransi Untuk Mencapai Kebebasan Finansial. Fintech: Journal of Islamic Finance, 3(1), 75–89.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gede Rama Darma Wijaya, R. Mohamad Atok
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
