Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jambura Journal of Mathematics Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
A’la, Kevina Alal
Unknown Affiliation
Computer Science & IT Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Metode Bayesian untuk Menghitung Premi Produk Asuransi Kendaran Bermotor dengan Pendekatan Monte Carlo Markov Chain Situmorang, Boy Nathanael; A’la, Kevina Alal; Arvianti, Aurellia; Yusuf, Feby Indriana; Handamari, Endang Wahyu
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32930

Abstract

Accurate premium determination is a fundamental aspect of risk management in motor vehicle insurance. This study implements the Bayesian method using a Markov Chain Monte Carlo (MCMC) approach to calculate the net premium. The aggregate claim model is constructed from a claim frequency distribution (Poisson) and a claim severity distribution (Generalized Extreme Value (GEV)), with the GEV distribution specifically chosen to model extreme claim risk. The analysis utilizes generated data for the period 2018–2024, with parameters derived from the historical data of PT Asuransi Jasa Indonesia Purwokerto (2013–2017). Parameter estimation, performed via OpenBUGS software, was validated to have achieved good convergence (MC-error   ). Based on the estimated parameters, a premium of IDR 397.502.000 was obtained, calculated using the net premium principle from the expected value of aggregate claims. These results demonstrate that the Bayesian MCMC approach is effective for producing a robust premium estimation, contributing a pricing framework that explicitly accounts for extreme value claims.
Implementasi Metode Extreme Value Theory untuk Menghitung Maksimal Kerugian Akibat Bencana Alam Yusuf, Feby Indriana; A’la, Kevina Alal; Thalita, Bella Cindy
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35193

Abstract

This study employs Extreme Value Theory (EVT) using the Block Maxima (BM) approach and the Generalized Extreme Value (GEV) distribution to model and estimate the potential maximum financial losses caused by natural disasters in Central Java, Indonesia. Historical loss data from 2022 are utilized to calibrate GEV distribution parameters, followed by Monte Carlo simulations to project risks over a 12-year horizon. The results reveal that the data exhibit heavy-tailed characteristics (indicated by a positive shape parameter), signaling significant extreme risks. Goodness-of-fit tests, specifically Kolmogorov-Smirnov and Anderson-Darling, confirm the validity of the GEV model. Return level analysis indicates a sharp escalation in risk; for a 100-year return period, potential losses reach a substantial magnitude. These findings contribute methodologically to regional fiscal risk estimation and underscore the necessity of precise financial mitigation instruments.
Co-Authors Arvianti, Aurellia Endang Wahyu Handamari Feby Indriana Yusuf Situmorang, Boy Nathanael Thalita, Bella Cindy