Optimalisasi Prediksi Klaim Kendaraan Bermotor Menggunakan Time Series Analysis dengan Pendekatan Variasi Kalender dalam Perusahaan Asuransi Umum - Gede Rama Darma Wijaya; R. Mohamad Atok (METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, 2026)
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.