Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Majalah Ilmiah Matematika dan Statistika (MIMS)
Ferry Jaya Permana
Jurusan Matematika, Fakultas Teknologi Informasi dan Sains Universitas Katolik Parahyangan, Bandung
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PARAMETER ESTIMATION OF LOGNORMAL AND PARETO TYPE I DISTRIBUTIONS USING FREQUENTIST AND BAYESIAN INFERENCES Then, Jenisha; Permana, Ferry Jaya; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp141-152

Abstract

Extreme events are events that rarely occur but they cause substantial losses. Insurance companies need to take extreme events into account in risk management because extreme events can have a negative impact on the company's financial health. As a result, insurance companies need an appropriate loss model that matches the empirical data from these extreme events. A distribution that is heavy-tailed and skewed to the right is a good distribution for modeling the magnitude of losses from extreme events. In this paper, two distributions with heavy tails and skew to the right will be used to model the magnitude of losses from extreme events, namely the lognormal distribution and the Pareto distribution type I. The parameters of these distributions are estimated using two inferences, namely the frequentist and Bayesian inferences. In the frequentist inference, two methods are applied, namely the moment method and maximum likelihood. On Bayesian inference, two prior distributions are used, namely uniform and Jeffrey. Test model suitability is carried out by visually comparing the model distribution function with the empirical distribution function, as well as by comparing the Root Mean Square Error (RMSE) value. The visualization results of the distribution function and RMSE values ​​show that in general, the Bayesian inference is better at estimating parameters than the frequentist inference. In the frequentist inference, the maximum likelihood method can provide better estimated values ​​than the moment method. In the Bayesian inference, the two prior distributions show a relatively similar fit to the data and tend to be better than the frequentist inference.
Pemodelan banyaknya kematian berdasarkan kasus konfirmasi COVID-19 di Indonesia, Malaysia, Thailand, dan Filipina menggunakan model linear tergeneralisasi Ha, Marlyn; Permana, Ferry Jaya; Yong, Benny
Majalah Ilmiah Matematika dan Statistika Vol. 25 No. 2 (2025): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v25i2.53694

Abstract

In early 2020, the COVID-19 disease, caused by the SARS-CoV-2 virus infection, became a global pandemic impacting the entire world, including Indonesia. To monitor the spread of COVID-19 and determine appropriate strategies to mitigate its impact, the World Health Organization (WHO) routinely reported confirmed case data and death case data due to COVID-19. Mathematical modeling can help understanding the relationship between the number of deaths based on daily confirmed cases. One simple mathematical model is the linear regression model. The linear regression model requires the assumption of homoscedasticity, and when this assumption fails, linear regression cannot be used. In this research, a generalized linear model (GLM) is used to address the shortcomings of the linear regression model. This research will predict the number of daily deaths based on daily confirmed case data using GLM based on historical data from Indonesia, Malaysia, Thailand, and Philippines. The functions used to describe the relationship between predictor and response variables include normal or Gaussian, Poisson, gamma, and negative binomial distributions. To evaluate whether the model fits the data, we used Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC). Additionally, the goodness of fit of the model in predicting the number of deaths is measured by finding the mean squared error (MSE). The best model is determined by considering the smallest AIC, BIC, and MSE values. The simulation results show that the GLM using the normal distribution is the best model in Indonesia, Malaysia, and Philippines, while the GLM using the negative binomial distribution is the best model in Thailand. Using the GLM, it was found that deaths occurred 14 days after a patient was confirmed with COVID-19 in Indonesia, 11 days in Malaysia, 12 days in Thailand, and 13 days in Philippines. Keywords: COVID-19, GLM, AIC, BIC, MSEMSC2020: 92C60, 62P10, 62J02, 62F10
FLOOD REINSURANCE PREMIUM PRICING BASED ON THE STANDARD DEVIATION PRINCIPLE WITH POT-BASED THRESHOLDS FOR MORTALITY AND PROPERTY DAMAGE RISKS Anggriawan, Vanessa; Permana, Ferry Jaya; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0347-0366

Abstract

Disasters that occur in Indonesia lead to financial loss. One approach to mitigating the financial impact is through the utilization of natural disaster insurance. Although natural disasters occur with a relatively small frequency, the associated losses are substantial. Insurance companies need to carefully consider the characteristics of natural disaster data, as these events can lead to significant claims and potentially result in the bankruptcy of insurance companies. Insurance companies can reduce the risk of bankruptcy by transferring some risk to reinsurance companies. In this paper, the disaster reinsurance premium is determined by considering both the mortality and economic risks using the peaks over threshold (POT) model under the standard deviation principle. The Poisson, generalized Pareto, and lognormal distributions are used to determine the premium, with parameters estimated using the maximum likelihood method. A simulation analysis is conducted using synthetic data generated with RStudio software, which includes the frequency of floods per year over 20 years, as well as the number of deaths and the number of houses damaged in each flood event. The threshold is determined using the percentage method, where 10% of the data is considered extreme values. The POT model is applied to various retention cases. The simulation results show that the risk of the number of damaged houses has a greater impact on the premium amount that the insurance company must pay to the reinsurance company than the risk of the number of deaths. Additionally, cases with retention values below the threshold result in the highest reinsurance premiums, while cases with retention values above the threshold result in the lowest reinsurance premiums. This paper also shows that the reinsurance premium changes almost linearly with the increase in the extreme value percentage. This study is among the first to apply the peaks over threshold model in combination with multiple distributions for reinsurance premium estimation in the Indonesian context. The findings provide new insights into the sensitivity of reinsurance premiums to damage thresholds and retention levels, offering a practical tool for insurers in disaster-prone regions.
Co-Authors Anggriawan, Vanessa Benny Yong Ha, Marlyn Then, Jenisha