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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 250 Documents
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Modeling Fire Insurance Claim Frequency Using Negative Binomial Regression Apdie Seno, Nathaniela; Brilliantxa Hazel Alvarivano
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1116

Abstract

Fire insurance plays an important role in providing financial protection against losses caused by fire risks. To support risk management and accurate premium determination, a model capable of predicting claim frequency based on relevant factors such as the Total Sum Insured (TSI) is required. The data used in this study consist of statistical fire insurance data covering the number of policies and claim frequencies in three provinces: West Java, Central Java, and East Java. The analysis was conducted using Poisson regression and Negative Binomial regression to model and predict claim frequency based on TSI. Initial estimation using the Poisson model indicated the presence of overdispersion, suggesting that this model is less suitable for the data. Therefore, the Negative Binomial regression model was applied, as it can better handle excessive variance. This model produced a lower AIC value compared to the Poisson model and showed that TSI has a significant effect on claim frequency. Thus, the Negative Binomial regression model is considered more accurate for predicting fire insurance claim frequency based on TSI.
Comparative Analysis of the Altman Z-Score and Springate Models in Predicting Bankruptcy of Pharmaceutical Companies in Indonesia Rahma, Yenita Indahyana; Benedicta, Hellena; Novita, Gaby
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1120

Abstract

The pharmaceutical industry is an important sector that not only focuses on making profit but also has a social responsibility to support public health. During the COVID-19 pandemic, this industry became much more active, especially in importing raw materials and producing medicines and health supplements. However, this growth also came with a large increase in debt, which raised the risk of financial problems. To deal with this, several bankruptcy prediction models have been developed, such as the Altman Z-Score and Springate models. These models are often used as early warning systems in many industries. Even so, research on bankruptcy prediction in Indonesian pharmaceutical companies is still limited. Therefore, this study aims to compare the two models in predicting bankruptcy in Indonesian pharmaceutical firms. The results show that the Altman Z-Score model is more suitable for long-term prediction, while the Springate model works better for short-term prediction.
Comparison of Poisson and Negative Binomial Regression Models in Identifying Factors Influencing Covid-19 Deaths in Indonesia. Nisa, Nabilla Rida Tri; Amanatullah Pandu Zenklinov; Husna Afanyn Khoirunissa; Nur Rezky Safitriani; Erlyne Nadhilah Widyaningrum; Rizka Amalia Putri; Morina A. Fathan
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1126

Abstract

This research compares Poisson Regression and Generalized Negative Binomial (GNB) Regression to underscore the factors that influence the growth of COVID-19 deaths in Indonesia. Count data such as mortality cases often violates the Poisson assumption of equidispersion (null mean equals variance) causing overdispersion. The GNB model is suggested as a remedy for overdispersed data crime prevention has become increasingly necessary for systematic development because secondary data from the Indonesian government has included dependable variables such as mortality rates for people aged over 60, diabetes mellitus, heart disease, lung disease, healthcare worker percentages, referral hospitals, and the population. The Poisson Regression reported R² of 87.67% and experienced overdispersion (θ₁ = 356.27, θ₂ = 417,597). The GNB model, in contrast, with a lower AIC (499.5566), overtook Poisson. Important factors that had significant impact on both models were mortality rates for individuals over 60, diabetes mellitus, healthcare workers, and referral hospitals, whereas heart and lung disease mortality rates were the ones that were not material. The GNB model had a better fit and tackled the issues of overdispersion in the Poisson Regression.
Forecasting Global Palm Oil Prices Using Fuzzy Time Series Markov Chain Approach Rahmat, Usep; Sutedjo, Yenni
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1128

Abstract

Global palm oil prices exhibit a fluctuating pattern, characterized by both upward and downward movements that are influenced by changes in stock levels as well as global demand. These factors are inherently difficult to predict with precision, creating challenges for palm oil entrepreneurs in formulating effective business strategies. To design appropriate strategies, entrepreneurs require comprehensive information on global palm oil prices, including historical, current, and projected data. Moreover, the level of forecasting accuracy is an essential consideration to ensure that the strategies developed are both reliable and effective. This study aims to forecast global palm oil prices using the Fuzzy Time Series–Markov Chain method and to evaluate the predictive accuracy of the resulting price estimates. The dataset used in this research consists of secondary data, namely global palm oil price records spanning the period from December 19, 2024, to March 13, 2025, comprising 50 observations obtained from the id.investing website. The analysis produced forecasted global palm oil prices for the subsequent three-day period, namely March 17-19, 2025, with predicted values of 4533.25; 4513.82; and 4530.37 (in MYR), respectively. The model achieved a Mean Absolute Percentage Error (MAPE) of 0.81%, corresponding to a forecasting accuracy rate of 99.19%.
Volatility Spillover between the IDR-USD Exchange Rate and Sectoral Stock Indices using EGARCH Model Putri Zahra Helena; Dwi Susanti; Budi Nurani Ruchjana
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1132

Abstract

The exchange rate is an important indicator in an open economy such as Indonesia, where fluctuations in currency values, particularly the USD against the IDR, have a significant impact on the Indonesian financial market. Sectoral stock indices, as one of the components of the financial market, reflect the specific financial conditions of each sector in Indonesia. The USD, as the dominant global currency, influences various sectors, especially those related to foreign exchange transactions. The relationship between exchange rates and sectoral stock indices may indicate interdependence in volatility or the presence of volatility spillover effects. Therefore, this study aims to identify the presence of volatility spillover between the IDR-USD exchange rate and three sectoral stock indices in Indonesia, namely the financial sector (JKFINANCE), the energy sector (JKENERGY), and the infrastructure sector (JKINFRA). The Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model is used to capture asymmetric volatility. Based on the estimation results, the best-fitting EGARCH models are EGARCH(2,3) for the IDR-USD exchange rate, EGARCH(3,1) for the financial sector, EGARCH(2,4) for the energy sector, and EGARCH(2,5) for the infrastructure sector. The γ parameters from these models indicate that bad news increases volatility in the exchange rate, financial sector, and energy sector, while good news has a greater impact on volatility in the infrastructure sector. Furthermore, Granger Causality tests on the residuals of the EGARCH models reveal the existence of volatility spillovers between the IDR-USD exchange rate and the financial, energy, and infrastructure sector.
Determining the Optimum Replacement Time of Dosing Pump Components Using the Age Replacement Model (Case Study at PDAM Tirtawening Bandung) Nahar, Julita
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1138

Abstract

The increase in population over time has a direct impact on the rising demand for clean water supply services, making the availability and management of water resources an increasingly critical aspect. To maintain water quality and supply continuity, reliable production machines are required. One of the machines used is a dosing pump, whose critical component is the valve ring. To ensure continuous operation without machine failure during the production process, appropriate maintenance is required by determining the optimum replacement time interval for the valve ring component using the Age Replacement model. The results of the data analysis show that the failure of the valve ring component follows a Nonhomogeneous Poisson Process (NHPP) with a Power Law Process (PLP) failure model. The optimum replacement time for the valve ring component based on the Age Replacement model is every 103.87 days of operation with a total replacement cost risk of Rp. 925,063.20, and this model is able to reduce the replacement cost of the valve ring component by 46.72%.
Estimation of an Optimal Portfolio Using the Constant Correlation Model: An Empirical Study on IDX Bisnis-27 Stocks Jehan Rizky Faustina Hartono; Anastasia Audrey Wijaya; Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1142

Abstract

Portfolio optimization is an essential aspect of investment decision-making, as investors aim to achieve an optimal trade-off between expected return and risk. However, the traditional Markowitz portfolio model requires the estimation of a large variance–covariance matrix, which becomes computationally complex as the number of assets increases. To address this limitation, this study applies the Constant Correlation Model (CCM), which simplifies portfolio construction by assuming a constant correlation among asset returns. This study aims to estimate an optimal stock portfolio using the CCM approach based on stocks included in the IDX Bisnis-27 index, representing companies with strong business fundamentals listed on the Indonesia Stock Exchange. The data consist of daily closing prices of 28 stocks for the period from January to December 2023. The analysis involves calculating stock returns, expected returns, standard deviations, Excess Return to Standard Deviation (ERS), constant correlation, and the cut-off rate (C*). The results show that the average constant correlation among the selected stocks indicates a moderate level of interdependence, suggesting that diversification benefits still exist. Based on the CCM selection criteria, only one stock, ANTM, has an ERS value exceeding the cut-off rate and is therefore included in the optimal portfolio with a weight of 100%. These findings indicate that ANTM exhibits the strongest risk-adjusted performance among IDX Bisnis-27 stocks during the observation period. This study provides practical insights for investors in constructing optimal portfolios using simplified correlation assumptions in emerging markets.
The London Exodus: A Spatial Analysis of Public Health Shocks and Economic Drivers of Internal Migration During the COVID-19 Pandemic Alexander Eko Nugroho; Zaki Ahmad; Muhammad Rizan Ramandika
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1143

Abstract

The COVID-19 pandemic in 2020 triggered a significant urban exodus in major global cities, shifting internal migration patterns in unprecedented ways. This study investigates the determinants of internal net migration across 33 London boroughs to understand the drivers behind this population displacement. Utilizing a spatial econometric framework, the research integrates public health indicators (COVID-19 cases), economic variables (housing prices and GDP), and geographic characteristics (population density and distance to the city center) into an enriched Ordinary Least Squares (OLS) model. The methodology is validated through rigorous diagnostic testing, including Heteroskedasticity-Consistent (HC1) standard errors and spatial autocorrelation checks (Moran's I), confirming that spatial feature engineering sufficiently captures geographic dependencies. The results reveal that the public health shock was the primary driver of the exodus; total COVID-19 cases exhibited a highly significant negative correlation with net migration, indicating that boroughs with higher infection burdens experienced larger population outflows. Population density acted as a secondary push factor, while traditional economic drivers such as housing prices and regional GDP were statistically insignificant. These findings suggest a temporary paradigm shift where immediate health security outweighed economic maximization in residential location decisions during the crisis.
Optimal Control Strategies for a Hoax Transmission Model Fatuh Inayaturohmat; Mochammad Andhika Aji Pratama; Aisyah Hanifah; Retta Farah Pramesti
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1146

Abstract

Recently, information spreads swiftly and widely through social media and other online platforms. However, this rapid flow of information is often followed by an increasing circulation of inaccurate or misleading content, commonly known as hoaxes. A hoax refers to false, deceptive, or unfounded information that is spread either intentionally or unintentionally. Typically, hoaxes are crafted in such a way that they appear to be credible news, with the aim of influencing public perception, spreading disinformation, or gaining political or economic advantages. This research investigates the spread of hoaxes within human populations based on a transmission model developed in earlier studies. The main contribution of this work lies in refining the model by incorporating an education parameter as a control strategy to identify the optimal level of education required to reduce the dissemination of hoaxes. The optimal control approach applied is the Pontryagin minimum principle. The model also takes into account both asymptomatic and symptomatic infected individuals, including the transition from asymptomatic to symptomatic cases. Numerical simulations demonstrate that applying this control strategy results in a faster decrease in the number of symptomatic infected individuals compared to conditions without any control intervention
Inventory Replacement Decision Support System Using Clustering and Analytical Hierarchy Process (AHP) Methods Nurjaman, Rusli; Tosida, Eneng Tita; Situmorang, Boldson Herdianto
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): 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.v6i4.1180

Abstract

The Center for Human Resources Development of Transportation Apparatus (PPSDMAP) still faces obstacles in manual inventory management, resulting in a long time required to determine which items are suitable or need to be replaced. This study aims to develop a web-based Decision Support System (DSS) to assist the inventory replacement decision-making process effectively and efficiently. The K-Means Clustering method was used to group inventory data based on age, condition, and value (price) attributes using 230 inventory data from January 1–November 30, 2023. The test results produced a Davies-Bouldin Index (DBI) value of 0.435 with six optimal clusters. Furthermore, the Analytical Hierarchy Process (AHP) method was used to determine the priority of handling strategies for less suitable or unsuitable inventory groups, with a Consistency Ratio (CR) below 10%, indicating a good level of consistency. The results of the study indicate that the developed system can assist PPSDMAP in grouping inventory objectively and support inventory replacement decision-making in a systematic, efficient, and measurable manner.

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