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Analisis Perhitungan Retensi Optimal Reasuransi Stop Loss dengan Metode Value at Risk (VaR) Oktavia, Grace; Addini, Fida Fathiyah; Prihandoko, Dedy Irawan
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 1 (2024): Sustainable Development Goal in Mathematics and Mathematics Education
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i1.3322

Insurance companies do not cover the entire risk of policyholders. The risk is generally transferred in part to the reinsurance company. Stop loss reinsurance is a form of reinsurance contract where there is a limit on the risk value that can be borne by the insurance company. This value is the retention value or retention limit in the reinsurance contract, which is the maximum risk or insurance value that can be borne by the insurance company. Determining the right retention value is very important. Optimizing the Value at Risk risk measure is one approach in calculating optimal retention. Optimal retention criteria were identified in this research so that optimal retention values could be calculated. This research uses a large claim data sample with the Weibull distribution. Determining optimal retention depends on the distribution of claim sizes and loading factors (additional factors in the insurance policy). With a 95% confidence level, for loading factors of 10%, 15%, and 20%, the estimated optimal retention values are $1,080.56, $1,393.54, and $1,567.20. This means that the risk transferred to the reinsurer is the remaining claim amount, if the claim size exceeds the optimal retention value.

Pengaruh Jumlah Pengajuan Penutupan dan Premi Asuransi Kebakaran terhadap Laba Bersih pada PT. Bosowa Asuransi Cabang Jakarta Kota Laksono, Ferdi; Kurniawan, Y. Jhony; Addini, Fida Fathiyah
Premium Insurance Business Journal Vol. 11 No. 1 (2024): PREMIUM INSURANCE BUSINESS JOURNAL
Publisher : P3M Trisakti School of Insurance (TSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35904/premium.v11i1.58

This research aims to analyze the influence of the number of closing applications and fire insurance premiums on the net profit of PT. Bosowa Insurance Jakarta City Branch. This research uses quantitative data analysis methods by collecting data from insurance transaction records from June 2022 to April 2024. The collected data is analyzed using multiple linear regression methods to identify the relationship between the independent variables (Number of Closing Applications and Fire Insurance Premiums) and the dependent variable (Profit Clean). The results of the research show that there is an influence of the Number of Closing Applications on Net Profit, while the effect of Fire Insurance Premiums on Net Profit is temporary and not strong. The implication of this research is the importance of management in managing the number of Closing Applications and Fire Insurance Premiums to increase Net Profit at PT. Bosowa Insurance Jakarta City Branch.

Klasifikasi Sifat Cidera Kecelakaan berdasarkan Karakteristik Masyarakat di Jakarta Sarinah, Sarinah; Addini, Fida Fathiyah; Agusinta, Lira; Haryanto, Dwi; Maolani, Rukaesih Achmad; Abdurachman, Edi
Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Vol. 11 No. 3 (2024): NOVEMBER
Publisher : Institut Transportasi dan Logistik Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54324/j.mtl.v11i3.1450

Traffic accidents are one of the biggest causes of death in Indonesia. Traffic accidents could occur due to various factors such as human factors, facilities, infrastructure, and the environment. These factors are seen as causes of traffic accidents. Furthermore, the impacts experienced due to traffic accidents are not always the same. Someone could be injured or even die. This impact is seen as the nature of an accident injury. This research analyzes and models the relationship between the nature of traffic accident injury variables and the variables causing traffic accidents. The data used in this research is 12,353 traffic accident data in DKI Jakarta from 2020 to 2021. The analysis was carried out using two main methods, namely the chi-square test to determine the existence of a relationship between variables and an ordinal logistics regression model to model the influence of variables causing traffic accidents on variables of the accident injuries nature. Based on the chi-square test, there is a significant relationship between the level of traffic injury and the factors of age, gender, type of work, time and location of the incident. However, there is no significant relationship between the level of the traffic injury and the day of the incident. Variables that are significantly related are then created as an ordinal logistics regression model equation. The results show that the injury level could be predicted based on the causal factors with 92.45% accuracy in classifying the level of the injury.

Analisis Perhitungan Retensi Optimal Reasuransi Stop Loss dengan Metode Value at Risk (VaR) Oktavia, Grace; Addini, Fida Fathiyah; Prihandoko, Dedy Irawan
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 1 (2024): Sustainable Development Goal in Mathematics and Mathematics Education
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i1.3322

Insurance companies do not cover the entire risk of policyholders. The risk is generally transferred in part to the reinsurance company. Stop loss reinsurance is a form of reinsurance contract where there is a limit on the risk value that can be borne by the insurance company. This value is the retention value or retention limit in the reinsurance contract, which is the maximum risk or insurance value that can be borne by the insurance company. Determining the right retention value is very important. Optimizing the Value at Risk risk measure is one approach in calculating optimal retention. Optimal retention criteria were identified in this research so that optimal retention values could be calculated. This research uses a large claim data sample with the Weibull distribution. Determining optimal retention depends on the distribution of claim sizes and loading factors (additional factors in the insurance policy). With a 95% confidence level, for loading factors of 10%, 15%, and 20%, the estimated optimal retention values are $1,080.56, $1,393.54, and $1,567.20. This means that the risk transferred to the reinsurer is the remaining claim amount, if the claim size exceeds the optimal retention value.

Prediction analysis of the length of time for changes in the status of positive patients for covid-19 delta and omicron variants using the markov chain model Haryanto, Dwi; Addini, Fida Fathiyah
Desimal: Jurnal Matematika Vol. 5 No. 2 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v5i2.13567

In this research, a Markov chain model was constructed from the conditions that might be experienced by the people during the Delta or Omicron variant of the COVID-19 pandemic. This condition is divided into 3 states; "0" indicates a healthy condition, "1" indicates the state of being infected with COVID-19, and "2" indicates the death state. Furthermore, from the model obtained, a transition probability matrix is made to determine the transition probability value and calculate the average number of steps needed to get to the death state. From the results of the analysis, the probability of transition to a state of death is 1. This shows that a person will surely die from being healthy or positive for COVID-19 within a certain time. During the Delta variant of the COVID-19 pandemic, the average time that a person reaches a death state from a healthy state is 34.02 years. Meanwhile, the average time taken for someone infected with the Delta variant of COVID-19 to death is 33.79 years. During the Omicron variant of the COVID-19 pandemic, the average time that a person reaches a death state from a healthy state is 37.63 years. Meanwhile, the average time taken for someone infected by the Omicron variant of COVID-19 to death is 37.41. This shows that the average age of a person infected with the Delta variant of COVID-19 has decreased by 2.79 months, while the average age of a person infected with the Omicron variant of COVID-19 has decreased by 2.70 months.

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