International Journal of Quantitative Research and Modeling
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
Articles
373 Documents
<![CDATA[The Influence of Information Technology Based Audit Procedures and Audit Experience on Audit Quality ]]>
<![CDATA[Neni Maryani]]>;
<![CDATA[Rendi Kusuma Natita]]>;
<![CDATA[Ali Rahman Reza Zaputra]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v4i4.546
<![CDATA[The Ministry of Finance as one of the regulators in Indonesia has also issued regulation Number 186 /PMK.01/2021 concerning the Development and Supervision of Public Accountants which is effective on March 15 2022 (Janah et al., 2022). This regulation did not appear without reason. Several cases related to audited financial statements have occurred in recent years. Some of the financial reports that are cases include the financial reports of PT. Garuda Indonesia Tbk which was audited by the Public Accounting Firm Tanubrata Sutanto Fahmi Bambang and Partners which is also an international affiliated Public Accounting Firm, namely BDO International, where the financial reports of PT (Setiono et al., 2020).]]>
<![CDATA[Analysis of Determining The Cost of Replanting for Smallholder Oil Palm Plantations Using Annuities Model with Python ]]>
<![CDATA[Rayyan Al Muddatstsir Fasa]]>;
<![CDATA[Herlina Napitupulu]]>;
<![CDATA[Sukono Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v4i4.547
<![CDATA[Palm oil replanting is a necessary activity to enhance the productivity of aging oil palm trees. However, the high costs associated with replanting often create a financial burden for farmers. To address this issue, the study proposes the implementation of a contribution or levy system for smallholder farmers while their oil palm plantations are still productive, which would alleviate the financial burden of replanting. The research methodology employed includes a literature review and primary data collection through a survey of smallholder farmers, with the data being processed to create a mathematical model and simulated using the Python programming language. The results of this study include the development of a mathematical model for the levy and distribution of replanting costs, along with a simulation of the proposed system. This model could help smallholder farmers prepare for replanting costs, enhance the sustainability of palm oil production, and ultimately increase productivity.]]>
<![CDATA[Development of Interactive Virtual Tour Based on 360-Degree Panorama Technology at the Bandung City Museum ]]>
<![CDATA[M. Ryzki Wiryawan]]>;
<![CDATA[Salwa Siti Nuraisyah]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v4i4.548
<![CDATA[Virtual tours have been widely used to introduce sites or buildings, but not much in education and tourism fields. The Covid-19 Pandemic has encouraged the growth of digitalization in all fields, including the development of museum virtual tours as an effort to provide visitor access among various restrictions. Along with these developments, the Bandung City Museum seeks to present an interesting museum virtual tour that can provide the best experience to its user. The purpose of this research is to find out the development and evaluation of an Interactive Virtual Tour Based On 360-Degree Panorama Technology At The Bandung City Museum. The research method used is Research and Development, with an analysis of potential and problems to improve the final product. The Virtual Tour allows users to navigate around the Museum building and listen to the narration, as well as read brief information about each object. For improvement, the virtual tour was also evaluated for usability by the users. Overall responses show that the virtual tour received positive feedback and was able to create satisfaction for the users, with several things to improve such as sound, images, and navigation quality.]]>
<![CDATA[Mitigation of The Risk of Failed Harvest Pond Farming Fisheries Using The Calculating of The Premium Through The Approach to The Principle of Expectation Value ]]>
<![CDATA[Fadia Irsya Septiana]]>;
<![CDATA[Dwi Susanti]]>;
<![CDATA[Sukono Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v4i4.551
<![CDATA[Pond cultivation is a promising business to be engaged in at this time, the demand for fish in the market is high, it opens opportunities for entrepreneurship in the field of fish cultivation. It is undeniable that several factors will cause crop failure in fish farming, both from weather factors and in the livestock process. If such an unexpected thing happened, the cultivators would be slightly affected. Therefore, there is a need for special insurance to protect farmers from financial losses due to possible risks, namely Fishery Microinsurance. This study aims to determine a reasonable amount of insurance premiums for small-scale shrimp pond aquaculture cultivators using the expectancy value principle calculation method. The data on the number of events uses the Poisson distribution, while the loss data uses the Exponential distribution in Pandeglang Regency. Next use the Maximum Likelihood Estimation method to calculate the parameter estimate. After that, the results of the parameter estimation are used to search for a collective risk model. Thus, the result of the premium calculation in this study was Rp 25.893.046]]>
<![CDATA[Application Of Survival Analysis: A Case Study Of The Nelson-Aalen Method In Patients With Kidney Failure ]]>
<![CDATA[Agung Prabowo]]>;
<![CDATA[Uni Febriani]]>;
<![CDATA[Isna Fingky Musita]]>;
<![CDATA[Valysha Alhamdaniya Rozak Putri]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1081
<![CDATA[Survival analysis is a statistical method used to evaluate the time an event occurs, particularly in censored data. In this study, the Nelson-Aalen method was applied to analyze the survival probability of patients with kidney failure based on medical data from Hasanuddin University Hospital. This method was used to calculate the cumulative hazard and survival function of patients. The analysis results showed that the longer a patient was treated, the greater the chance of recovery. The Nelson-Aalen method was considered more efficient in handling small samples compared to other non-parametric methods. This study provides important insights for healthcare practitioners in planning patient care and improving their quality of life.]]>
<![CDATA[GARCH-M Approach for Energy Stock Volatility Estimation in the LQ45 Index ]]>
<![CDATA[Adeliya Fernanda]]>;
<![CDATA[Chairamanda Binar Gunawan Gunawan]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1118
<![CDATA[Stock price movements in the energy sector in Indonesia often exhibit high volatility, especially for stocks listed in the LQ45 Index. This high volatility is caused by vulnerability to fluctuations in global commodity prices, energy transition issues, and regulatory changes, which in turn pose challenges in investment decision-making and risk management. This study aims to estimate the volatility of energy stocks in the LQ45 Index using the Generalized Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) approach. This model was chosen because of its ability to dynamically model return variance (volatility) while linking it to expected returns, thus enabling a direct analysis of the risk-return trade-off. The data analyzed are daily returns from January 17, 2020, to December 27, 2024, for eight energy stocks in the LQ45 Index: ADRO, BRPT, ITMG, MEDC, PGAS, PGEO, PTBA, and UNTR. The analysis was conducted by building the most optimal GARCH-M model and evaluating the estimation results through statistical criteria. The research results are expected to demonstrate significant volatility persistence in energy stocks, while providing strong evidence of the link between increased risk and expected returns. Therefore, the application of the GARCH-M model is expected to make a significant contribution to understanding the risk-return dynamics in the domestic energy sector and provide a basis for investors and portfolio managers in formulating more adaptive strategies.]]>
<![CDATA[Age-specific Age-specific Female Population Projection of Bangladesh in 2026 and 2031Using Leslie Matrix Model ]]>
<![CDATA[Nurain Arju]]>;
<![CDATA[Khandker Farid Uddin Ahmed]]>;
<![CDATA[Rehena Nasrin]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1129
<![CDATA[The population density of Bangladesh is higher than many other countries in the world. According to the preliminary census report of the Bangladesh Census and Household Survey 2022, the total population of Bangladesh at the time of the 2022 census was 165,158,616. The total number of females and males was 8,33,47,206 and 8,17,12,824, respectively. As a country’s equilibrium in environment, economics, and even administration is highly influenced by its population, these demographic data are essential for a well-regulated impact. Population projection helps a country achieve its desired population for overall prosperity. A widely used model for projecting population growth is the Leslie model. In this research, Leslie's matrix model is applied to predict the age-specific female population of Bangladesh and population growth in 2026 and 2031 by using female survival and birth rates. The eigenvalues of the Leslie matrix are used to calculate the population growth rate of women, where the principal eigenvalues are taken as the desired growth rate, and the corresponding eigenvectors are used to obtain the age-specific female population. In this research, the results indicate a gradual decrease in the female population growth rate, with the principal eigenvalue estimated at approximately 0.99 for 2026 and 0.97 for 2031, suggesting a slow and steady decline.]]>
<![CDATA[Outlier-Resistant Claim Reserving Using a Robust Chain Ladder Method with a 2.5 Interquartile Range Criterion ]]>
<![CDATA[Naia Rafida Mumtaz]]>;
<![CDATA[Jessica Sie]]>;
<![CDATA[Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1144
<![CDATA[Claim reserving plays an essential role in evaluating outstanding liabilities in credit insurance portfolios, which are commonly characterized by irregular claim development patterns and the occurrence of extreme claim values. Such characteristics may influence the stability of reserve estimates when historical paid claim data exhibit high variability across development periods. Therefore, an appropriate reserving approach is required to accommodate these data features while maintaining a consistent estimation process. This study estimates claim reserves for a credit insurance portfolio using the Robust Chain Ladder method based on paid claim data. The analysis is conducted by constructing incremental and cumulative run-off triangles from historical claim payments. To address the presence of extreme observations, a residual-based outlier detection procedure is applied using an interquartile range criterion with a 2.5 multiplier. This approach aims to reduce the influence of extreme claim values while preserving plausible large claims inherent in credit insurance portfolios. The reserving process is performed iteratively until no further extreme observations are identified, resulting in an adjusted run-off triangle used for reserve estimation. Based on the adjusted run-off triangle, the total estimated claim reserve amounts to IDR 120,773,423,681. This value represents an actuarial estimate of outstanding claim liabilities across all accident periods derived from historical claim development under the applied reserving assumptions. The results provide an overview of reserve levels for credit insurance portfolios and illustrate the application of a robust reserving approach in the presence of irregular and volatile claim patterns.]]>
<![CDATA[Implemetation of The ARIMA Model For Forecasting COVID-19 in Indonesia ]]>
<![CDATA[Ai Musrifah]]>;
<![CDATA[Fietri Setiawati Sulaeman]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1167
<![CDATA[Processing large-scale data is indispensable for today's information technology needs, because large-scale data processing can produce decisions that can be useful for formulating or planning strategies that produce knowledge can be used as decisions by top management in achieving organizational goals. As is the case in this new normal era where information about COVID-19 data is needed in the next few days. So this study was made to predict future COVID-19 data. By using methods in data science, large- scale data processing can facilitate the presentation of data to be understood, analyzed and viewed. This research is related to large- scale data processing by adjusting to current needs, namely COVID-19 data analysis based on time series analysis. The method used in this study is OSEMN. The steps carried out in this research are filtering the data, then visualizing the data, and finally forecasting from the existing data. This research was conducted with the aim of processing large-scale COVID-19 datasets into data that is easy to analyze and informative, visualizing COVID-19 datasets to make them easier to understand, and forecasting in the future. The dataset used was obtained from Kaggle entitled "COVID-19 data from John Hopkins University" uploaded by Anthony Goldbloom as Kaggle's CEO which contains confirmed data and death data from around the world. From the dataset, several countries in Southeast Asia, neighboring countries from Indonesia, and Indonesia were selected to explore. From the exploration results obtained various information from the data in the form of a DataFrame which is easy to analyze after Exploratory Data Analysis, various graphic plots that are easy to understand, and get forecasting results using ARIMA algorithm.]]>
<![CDATA[Comparison between Holt Winter Additive and Holt Winter Multiplicative Methods in Forecasting Bank Central Asia (BBCA) Stock Price in Indonesia Stock Exchange ]]>
<![CDATA[Alem Huga Martono]]>;
<![CDATA[Ruben Clynton Oey]]>;
<![CDATA[Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v7i1.1237
<![CDATA[Stock market investment plays a pivotal role in the Indonesian economy as a source of capital formation and wealth creation for investors. As a financial instrument, the performance of stock markets is determined by the ability to predict future price movements accurately to minimize investment risks and maximize returns. Bank Central Asia (BBCA) is one of the largest private banks in Indonesia and is strategically positioned as one of the most actively traded and liquid stocks in the Indonesia Stock Exchange (IDX), consistently included in the LQ45 index. This research aims to determine the proper forecasting method for the existing data patterns of BBCA stock prices and to provide more accurate forecasting results for investment decision-making. The methods used include Holt Winter Additive and Holt Winter Multiplicative exponential smoothing techniques. The dataset comprises daily closing prices of BBCA stock from December 2, 2024, to December 5, 2025, totaling 241 trading days. From these two methods, the forecasting accuracy was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The results show that the Holt Winter Additive method has the smallest MAPE value of 0.86% (MSE: 5,043.71) compared to the Holt Winter Multiplicative method with MAPE of 1.01% (MSE: 6,789.32), indicating that the Additive model provides superior forecasting performance for BBCA stock price prediction in the observed period.]]>
