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International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 390 Documents
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Net Single Premium Estimation for Credit Life Insurance under Floating Interest Rates Using the Cox-Ingersoll Ross (CIR) Stochastic Model and Amortization Method Azzah Nailah Salsabila; Muhammad Hanif Faridy
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1238

Abstract

Credit life insurance is designed to protect lenders against the risk of loan default arising from the death of borrowers during the loan period. In practice, premium determination for credit life insurance often assumes constant interest rates and does not fully account for demographic risk factors, which may lead to inaccurate pricing. This study aims to estimate the net single premium of credit life insurance by incorporating both borrower-specific mortality characteristics and floating interest rate dynamics under a stochastic framework. The loan interest rate is assumed to follow a floating structure linked to the BI 7-Day Reverse Repo Rate, which is modeled using the Cox–Ingersoll–Ross stochastic interest rate model to capture mean-reverting behavior and ensure non-negative interest rates. Loan repayment is structured through a monthly amortization scheme, resulting in a decreasing insurance benefit equal to the outstanding loan balance at the time of death. Mortality risk is evaluated using the Indonesian Mortality Table IV, with monthly death probabilities derived under the Uniform Distribution of Death assumption to accommodate fractional-age valuation. The actuarial present value of insurance benefits is computed by discounting the outstanding loan balance for each month and weighting it by the corresponding probability of death. The expected value of this random present value yields the net single premium. Numerical illustrations demonstrate that premiums increase with borrower age and are higher for male borrowers than for female borrowers of the same age, reflecting underlying mortality differences. Furthermore, the use of floating interest rates leads to annual adjustments in loan installments, which directly influence the evolution of insured benefits and premium values. Overall, the results indicate that integrating stochastic interest rate modeling with demographic mortality structure produces a more accurate and risk-reflective estimation of credit life insurance premiums, particularly in environments where floating interest rates are applied.
Estimation of Stock Return Volatility Using Bayesian MCMC-Based Stochastic Volatility Model Muhammad Bahrul Ilmi; Hanan Hamuda
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1239

Abstract

Parameter estimation of a distribution can be performed through two main approaches: the classical method and the Bayesian method. The Bayesian method integrates the sample distribution with the prior distribution, where random sampling is conducted via simulation techniques such as Markov Chain Monte Carlo (MCMC) with the Gibbs Sampling algorithm. This algorithm works by constructing a Markov Chain through recursive sampling from the full conditional posterior distribution for each parameter until convergence is reached. This study applies the Bayesian method with MCMC using the Gibbs Sampling algorithm to estimate the parameters of the Stochastic Volatility model, which allows asset price volatility to vary over time. The obtained Stochastic Volatility model is then used to predict the stock returns of PT. Aneka Tambang Tbk. (ANTM.JK), where the prediction results show good conformity with actual data. The resulting prediction values can be utilized by investors as a reference in making optimal investment portfolio decisions.
Development of Kaplan Fixed Pitch Microhydro Turbine as a Renewable Energy Source at the TNI AD Border Post Koko Hadi Santoso; Pradika Noviandani; Suryaman
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1253

Abstract

Micro hydropower plants offer a promising renewable energy solution for remote areas lacking access to electricity, particularly military border posts. This study aims to design and develop a fixed pitch Kaplan turbine for low head micro hydro applications. The turbine operates under a head of 1.8 m and a discharge of 0.006 m³/s. The research methodology includes theoretical calculations, numerical simulations using Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), and experimental prototype testing. Key design parameters include runner diameter, blade configuration, shaft design, and transmission system. Simulation results show stable flow distribution and safe structural stress levels. Experimental results indicate a power output of 74.9 W with an efficiency of 70.7%. These findings demonstrate that the proposed turbine is suitable for low-head applications and can serve as an alternative energy source for remote military installations.
The Effect Of Addition Of Benzyl Amino Purine (BAP) and Bean Sprout Extract in Murashige and Skoog (MS) On The Growth Of Hybrid Cattleya Orchid Tissue Culture In Vitro Dewi, Elena Fitria; Budiono, Ruly; Muttaqin, Asep Zainal
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1293

Abstract

Cattleya orchids are highly sought-after ornamental plants, leading to the development of hybrid Cattleya orchids. However, conventional propagation of Cattleya orchids is considered difficult. Therefore, in vitro tissue culture is necessary as an alternative. Important factors in the success of tissue culture are the growing medium and plant growth regulators (PGRs), such as cytokinins and auxins. This study aimed to examine the effect of the addition of synthetic PGRs (BAP) and natural PGRs (bean sprout extracts) combined on the growth of hybrid Cattleya orchid tissue cultures in vitro. This study was conducted using a Completely Randomized Design experimental method with 5 levels of BAP concentration (0 ppm, 0.5 ppm, 1 ppm, 2 ppm, 3 ppm), bean sprout extract (0%, 5%, 7.5%, 10%, 12.5%) and a combination of BAP and bean sprout extract, namely control, BAP 0.5 ppm + 5% bean sprout extract, BAP 1 ppm + 7.5% bean sprout extract, BAP 2 ppm + 10% bean sprout extract, BAP 3 ppm + 12.5% ​​bean sprout extract on MS media. The parameters observed included plantlet height, number of leaves, number of roots, root length, and fresh weight. Data were analyzed using analysis of variance (ANOVA) and continued with Duncan's test. The results showed that the administration of BAP, bean sprout extract and their combination had a significant effect on the growth of plantlet height, number of leaves, number of roots, root length and fresh weight. The combination treatment of 2 ppm BAP and 10% bean sprout extract was effective in supporting the growth of hybrid Cattleya orchids.
The Effect Of Tomato Extract (Solanum lycopersicum L.) and Coconut Water (Cocos nucifera L.) as Growth Regulators (PGR) in Murashige And Skoog (Ms) Media on the Growth of In Vitro Crossed Cattleya Orchid Tissue Culture Satiyanto, Hanny Putri; Budiono, Ruly; Muttaqin, Asep Zainal
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1297

Abstract

Cattleya orchids are highly prized ornamental plants that can be significantly propagated using in vitro tissue culture techniques. PGRs significantly influences the success of tissue culture. Synthetic PGRs are commonly used, but are relatively expensive and not environmentally friendly, so natural materials need to be used as alternatives. The purpose of this study was to determine how tomato extract (Solanum lycopersicum L.), coconut water (Cocos nucifera L.), and their combination as natural PGRs affect the growth of hybrid Cattleya orchid plantlets on Murashige and Skoog (MS) media. A Completely Randomized Design (CRD) was used to treat tomato extract (0%; 1%; 1.5%; 2%; 2.5%) and coconut water (0%; 5%; 10%; 15%; 20%) and their combinations. This study analyzed data based on parameters of plantlet height, number of leaves, number of roots, root length, and wet weight using ANOVA analysis followed by Duncan's test at the 5% level. The results showed that several growth parameters were affected such as plantlet height, number of roots, root length, and fresh weight showed significant effects. The number of leaves did not have a significant effect. Natural PGRs are generally considered a more cost-effective and environmentally friendly alternative, their effectiveness has not completely replaced synthetic PGRs.
A CNN-Based Bimodal Biometric Authentication System Using Face and Iris Recognition Oluwakemi C. Abikoye; Ridwan Opeyemi Kasope; Kafayat Odunayo Tajudeen; Abimbola Ganiyat Akintola
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1318

Abstract

In the evolving landscape of information technology, this study presents a bimodal biometric authentication system that combines facial and iris recognition using advanced Convolutional Neural Networks (CNNs) to address the escalating security concerns of personal and sensitive information. The bimodal approach leverages the unique textures of facial and iris features to create a robust and secure authentication mechanism, demonstrating high accuracy (95.76%) and precision value of 97.83%, low False Acceptance Rate (2.54%) and False Rejection Rate (14.29%). The system framework integrates the strengths of both facial and iris modalities, mitigating vulnerabilities inherent in unimodal systems and advancing the field of biometric authentication by providing a resilient solution against emerging cyber threats, enhancing the reliability of user identification, and contributing to safer digital environments across various domains.
The Effect of Capital Adequacy Ratio (CAR), Net Interest Margin (NIM), and Operating Expenses to Operating Income (BOPO) on Profit Growth in Banking Companies Listed on the Indonesia Stock Exchange for the Period 2019–2024 Sarah Samratul Saadah; Aldy Agustian
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1320

Abstract

Centered around banking institutions listed on the Indonesia Stock Exchange from 2019 to 2024, this paper explores how net interest margin (NIM), capital adequacy ratio (CAR), and the operating ratio (BOPO) steer profit growth. By adopting a quantitative framework, we pulled secondary data directly from the firms' annual financial disclosures. A purposive sampling approach narrowed our scope down to 19 banks, yielding 114 distinct observations for analysis. To process this data, we utilized multiple linear regression coupled with classic assumption checks, coefficient of determination, plus F and t tests. Our analysis brought several insights to light. First, CAR yields a positive, meaningful pull on profit growth ($t = 3.412, p = 0.000$). Second, NIM similarly drives profit growth upward in a significant manner ($t = 3.081, p = 0.000$). On the flip side, BOPO exerts a clear negative drag on earnings growth ($t = -2.338, p = 0.001$). Taken together, these three elements simultaneously dictate profit shifts ($F = 19.409, p = 0.000$), explaining roughly 64.6% of the variance ($R^2 = 0.646$). Ultimately, keeping capital adequate, maximizing interest returns, and tightening operational efficiency stand out as the core pillars of bank profitability
Comparison of LSTM and ARIMA-GARCH Models in Predicting Stock Price Volatility of Islamic Banking in Indonesia Alim Jaizul Wahid; Rizki Apriva Hidayana; Nestia Lianingsih
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1327

Abstract

Stock price volatility is one of the most critical investment risk indicators, particularly in Islamic banking stocks that face the dual challenges of conventional capital market dynamics and compliance with Islamic principles. This study compares the predictive performance of two modeling approaches: Long Short-Term Memory (LSTM), a deep learning architecture based on Recurrent Neural Networks, and the classical econometric ARIMA-GARCH model, in predicting stock price volatility of four Islamic banking issuers in Indonesia, namely BRIS (Bank Syariah Indonesia), BSIM (Bank Sinarmas Syariah), PNBS (Bank Panin Dubai Syariah), and BTPNS (Bank BTPN Syariah), for the period 2019–2024. Daily closing price data was obtained from the Indonesia Stock Exchange (IDX). The ARIMA-GARCH model was built through the stages of identification, estimation, and Box-Jenkins diagnostic testing, while the LSTM model was optimized through hyperparameter tuning with a 60-day rolling window. Predictive performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the LSTM model consistently produces lower MAPE values than ARIMA-GARCH for all issuers studied, especially during periods of high volatility such as the COVID-19 pandemic (2020) and global interest rate instability (2022–2023). However, the ARIMA-GARCH model provides better interpretability and is more stable under calm market conditions. This research contributes to the literature on Sharia-based quantitative finance in Indonesia and provides practical implications for investors and risk managers.
The Impact of Macroeconomic Variables on Actuarial Premium Calculation: Evidence from Indonesian Life Insurance Using OLS Regression Alim Jaizul Wahid; Khandker Farid Uddin Ahmed; Renda Sandi Saputra
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1328

Abstract

Actuarial science requires accurate net premium calculations, yet traditional approaches often ignore macroeconomic dynamics such as inflation and interest rates. This study investigates the impact of macroeconomic variables specifically inflation rate and Bank Indonesia reference interest rate (BI Rate) on actuarial net premium calculations for term life insurance products in Indonesia. Using annual time-series data from 2005 to 2023, this study applies Ordinary Least Squares (OLS) regression to quantify the relationship between macroeconomic indicators and net premium values computed using the Indonesian Mortality Table (TMI-2019). The results indicate that inflation has a significant positive effect on net premium values, while the BI Rate exerts a significant negative effect, consistent with actuarial theory regarding the present-value discounting mechanism. The adjusted R-squared value of 0.847 confirms strong explanatory power of the model. These findings provide practical guidance for insurance companies and the Financial Services Authority (OJK) in setting premium reserves under dynamic macroeconomic conditions. This study contributes to the intersection of actuarial science and macroeconomic modeling, which remains underexplored in the Indonesian context.
Bankruptcy Probability Modeling Using Integro-Differential Equations With Gamma Distributed Claims Lulu Ulmufidah; Agung Prabowo; Niken Larasati
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1329

Abstract

This study aims to model and analyze the ruin probability of an insurance company using an integro-differential equation under the assumption that claim sizes follow a Gamma distribution. The research method employed is a literature study, conducted by reviewing relevant books and scientific articles. The modeling process is carried out using an integro-differential equation and simplifying it into the form of a homogeneous linear differential equation. Numerical computations are performed using Python programming, and the results are presented in tables and graphs to facilitate analysis. The model is developed in four cases based on variations in the parameters of the Gamma distribution. The results show that the ruin probability decreases as the initial capital and premium loading increase, and increases as the expected value of claims rises. Therefore, an increase in the insurance company’s surplus will reduce the risk of ruin.

Page 38 of 39 | Total Record : 390

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