International Journal of Mathematics, Statistics, and Computing
International Journal of Mathematics, Statistics, and Computing (IJMSC) is an official journal of the Communication in Research and Publications (CRP) and publishes original research papers that cover the theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). IJMSC will act as a platform to encourage further research in Mathematics, Statistics, and Computing, theory and applications. The rapid development of science and technology has had a significant impact on various aspects of human life, including in the fields of economy, education, culture and government. The positive impacts of science and technology include facilitating access to information and communication, accelerating production and service processes, as well as providing new business and investment opportunities. Mathematics, statistics, and computer science have a very important role for the advancement of science and technology. Among them are as a basis for computer programming, basic calculations in the development of modern tools, can solve a problem even with big data. The mission of the International Journal of Mathematics, Statistics, and Computing (IJMSC) is to enhance the dissemination of knowledge across all disciplines in theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). The above discipline is not exhaustive, and papers representing any other social science field will be considered. The IJMSC particularly encourage manuscripts that discuss the latest research findings or contemporary research that can be used directly or indirectly in addressing critical issues and sharing of advanced knowledge and best practices in Mathematics, Statistics, and Computing (MSC). The essential but not exclusive, audiences are academicians, graduate students, researchers, policy-makers, regulators, practitioners, and others interested in business, management, economics, and social development studies. For ensuring a wide range of audiences, this journal accepts only the articles in English. The scope of mathematics are: Algebra, Applied Mathematics, Financial Mathematics, Approximation Theory, Combinatorics, Computing in Mathematics, Operations Research Methodology, Discrete Mathematics, Mathematical Physics, Geometry and Topology, Logic and Foundations of Mathematics, Number Theory, Numerical Analysis, and other relevant matters. The scope of statistics are: Probability Theory, Central Limit Theorem Computation, Sample Survey, Statistical Modeling, Statistical Theory, Computational Statistics, Data Sciences, Actuarial Sciences, Regression Models, Time Series Models, and other relevant matters. The scope of computing are: Algorithms and Data Structures, Computer Architecture, Software Engineering, Artificial Intelligence and Robotics, Human and Computer Interaction, Informatics Organizations, Programming Languages, Operating Systems and Networks, Databases, Computer Graphics, Computing Science, BioInformatics, Information Technology, and other relevant matters.
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Analysis of Aggregated Claim Numbers with Geometric Distribution and Claim Sizes with Weibull Distribution Using Convolution Method
Maharani, Asthie Zaskia
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.178
An insurance claim is a form of claim from the insured party to the insurer, in this case the insurance company, which is submitted when a disaster or event that causes loss occurs. This claim is based on an agreement contract in the form of an insurance policy that has been agreed upon by both parties. Claims that arise every time a risk occurs are known as individual claims, while the total of individual claims that occur during a certain insurance period is called an aggregate claim. Aggregate loss refers to the total loss that must be borne by the insurance company due to claims filed by policyholders in a certain period. This study aims to estimate the total aggregate claim (aggregate loss) by modeling the number of claims using the Geometric distribution and the size of the claim using the Weibull distribution. The research was conducted using simulated data from PT Insurance XYZ. The method used in this research is the convolution method, which allows the calculation of the distribution of total aggregated claims based on the pairwise multiplication of the probability density function. To support the analysis, Easyfit and R Studio software were used in data processing and simulation. The results showed that the estimated total aggregate claim (aggregate loss) for a 12-month period on the simulated data was IDR2,809,454,000 using the Geometric distribution for the number of claims and the Weibull distribution for the size of the claim. In addition, the variance value obtained from the simulation results is 5.051215e-06. These findings provide an important overview of the estimation of potential losses that must be borne by insurance companies and can be used as a reference in risk management and the establishment of a more optimal financial strategy.
Application of Expected Loss (EL) for Loan Loss Estimation Based on Loan Term Using Simulation Data
Tenripada, Andi Sakinah Yan
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.179
This study aims to evaluate the effect of loan tenor on loan loss estimation using the Expected Loss (EL) model. Through this simulation data calculation, various scenarios with varying loan tenors show that loan tenors have a significant influence on the calculation of Expected Loss (EL). Longer tenors tend to increase the Expected Loss (EL) due to an increase in credit risk over time. The calculation results provide important implications for financial institutions in setting lending policies and managing credit risk.
Calculation of Value at Risk of Property Fire Losses in West Jakarta with the Extreme Value Theory Method
Putri, Linda Damayanti
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.180
Property fires are an inevitable disaster, but their impact can be minimized through proper risk management. In urban areas such as West Jakarta, with high population density and economic activity, fires often cause losses. In therange of 2014 to 2023, the peak of the loss occurred in 2019 of IDR 103,354,500,000. Soto avoid unwanted things, it is necessary to calculate the losses that may occur. The Extreme Value Theory (EVT) method is used in this study to analyze the risk of extreme losses. Using Peaks Over Threshold (POT), the estimated value at Risk (VaR) shows a maximum loss of IDR 86,245,771,176 (95% confidence level) and IDR 255,535,153,859 (99% confidence level). These results help manage fire insurance risks to reduce future economic impacts.
Estimation Model of Pure Health Insurance Premiums in Southeast America Using Generalized Linear Model (GLM) with Gamma Distribution
Putri, Aulya
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.181
Health insurance premiums are on the rise due to increasing medical costs, inflation, and the lingering effects of the COVID-19 pandemic. Accurate premium pricing is crucial for insurance companies to maintain financial stability and offer fair rates to policyholders. Generalized Linear Models (GLM) have been widely used in actuarial science for modeling insurance premiums. This study proposes the use of GLM with a Gamma distribution to model health insurance premiums. The Gamma distribution is suitable for non-negative and positively skewed data, which is characteristic of insurance claim amounts. By analyzing historical data from a Southeast United State insurance company, we aim to identify key factors influencing premium pricing and develop a robust premium model. The model will consider factors such as age, gender, BMI, number of children, and smoking status to predict individual risk profiles and determine appropriate premiums. Our findings indicate that age and smoking status are the most significant factors affecting premium rates. Older individuals and smokers tend to have higher premiums due to their increased risk of health issues. Gender and BMI, however, were found to have no significant impact on premium pricing in this specific dataset. Insurance companies can use the identified factors (age, smoking status, etc.) to create more precise risk profiles for their policyholders.
Optimization of Stock Portfolio in Indonesian Health Sector using Markowitz Modern Portfolio Theory
Kalfin;
Hidayana, Rizki Apriva
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.182
This study analyzes the optimization of the health sector stock portfolio on the Indonesia Stock Exchange using the Markowitz Modern Portfolio Theory method. The data used are the daily closing prices of health sector stocks over the last three years obtained through web scraping techniques from Yahoo Finance. The analysis includes the calculation of daily returns, daily risks, and covariance matrices between stocks. The results of the portfolio optimization show that out of the ten stocks analyzed, the optimal portfolio consists of four stocks, namely MIKA.JK (62.82%), KLBF.JK (15.58%), CARE.JK (15.37%), and SAME.JK (6.23%). This portfolio generates a daily return of 0.216% with a risk level of 1.996%. MIKA.JK contributes the highest return of 0.02063% with a risk of 1.52601%. This study provides guidance for investors in optimizing fund allocation in the health sector stock portfolio in Indonesia.
Prediction Of Cigarette And Tobacco Price Index In Tangerang City Using Ses And Double Linear Exponential Smoothing
Saudi, Jeremy Heriyandi
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications
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DOI: 10.46336/ijmsc.v3i1.183
The cigarette and tobacco price index is a crucial indicator that reflects changes in prices and demand in the tobacco market. Accurate predictions of this index are essential for the government and industry players in planning policies and business strategies. This study aims to forecast the cigarette and tobacco price index in Banten Province using the Single Exponential Smoothing (SES) and Double Linear Exponential Smoothing (DES) methods. The data used in this research comprises monthly cigarette and tobacco price index data from January 2021 to December 2023. SES and DES models are applied for prediction, and their results are evaluated using performance indicators such as Mean Absolute Percentage Error (MAPE). The research findings indicate that both methods are highly effective in predicting the cigarette and tobacco price index, with the SES method providing slightly more accurate predictions than the DES method. The MAPE error value for the SES method is 0.51%, while the DES method has a MAPE error value of 0.65%. These results are expected to contribute to policymakers and industry players in understanding price trends and making more informative decisions.