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
236 Documents
<![CDATA[Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method ]]>
<![CDATA[Pamungkas, Abram Chandra Aji]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Susanti, Dwi]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v%vi%i.594
<![CDATA[Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.]]>
<![CDATA[Annuity in Advance for Rental Properties: Profit and Risk Analysis for Owners of Student Rental Homes Near Campus ]]>
<![CDATA[Sabrina, Amirah Nur]]>;
<![CDATA[Nabila, Hella Rizwa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.830
<![CDATA[Nowadays, the rental homes business in the area around the campus offers significant profits for its owners, this is due to the large number of students who migrate so they choose to live in rental homess, but on the other hand this can also cause various risks, including fluctuations. Maintenance costs and high occupancy rates. This can make rental homes owners' income unpredictable and make it difficult to create long-term financial goals. Using an upfront annuity model, where the owner receives rental payments at the start of the period, is one way to lower this risk. rental homes owners can guarantee more consistent cash flow and make more accurate income predictions by applying this concept. The aim of this research is to examine how the application of the advance annuity model affects the income and risks of rental homes owners. This study will assess how advance annuities contribute to income stability and reduce the uncertainty that often occurs in rental homes operations by using comprehensive financial techniques. Apart from that, this analysis will also consider various external factors that can influence occupancy levels, such as campus policies and economic conditions. It is hoped that the findings from this research will provide useful insights for rental homes owners in maximizing profits while managing risks more effectively, so that they can adapt to ever-changing market dynamics. Therefore, this strategy can be a smart alternative for rental homes owners in optimizing their business performance around campus.]]>
<![CDATA[Markov Chain Method for Calculating Insurance Premiums ]]>
<![CDATA[Dihna, Elza Rahma]]>;
<![CDATA[Ismail, Muhammad Iqbal Al-Banna]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.817
<![CDATA[This study applies the Markov Chain method to calculate insurance premiums based on the dynamic health status of policyholders over time. The model considers three health states Healthy, Mild Illness, and Severe Illness each associated with a specific insurance premium. The transition probabilities between these states are represented in a transition matrix, capturing the likelihood of a policyholder remaining in their current health state or transitioning to another state in a given period. Using this approach, the steady-state distribution, which reflects the long-term probabilities of being in each health state, is calculated. This distribution is then used to determine the expected monthly premium by taking a weighted average of the premiums for each state. The methodology incorporates real-world scenarios where a policyholder's health condition may change over time, impacting the premiums they are required to pay. The Markov Chain model provides an effective framework for estimating these premiums by considering the "memoryless" nature of health state transitions, where future states depend only on the current state and not on prior health history. By solving the steady-state equations pi P=pi and ensuring the total probabilities sum to one, the model yields a robust estimation of long-term health state distributions. These distributions, combined with the associated premiums, produce an accurate calculation of expected insurance costs. The results demonstrate the flexibility and accuracy of the Markov Chain method in assessing risks and setting premiums. Insurers benefit from this approach as it enables dynamic pricing strategies tailored to individual risk profiles. For policyholders, the model provides transparency in understanding how health status influences premiums. Overall, this study highlights the practicality of using Markov Chains in health insurance pricing and underscores their importance in creating equitable and sustainable insurance systems.]]>
<![CDATA[Semantic Classification of Sentences Using SMOTE and BiLSTM ]]>
<![CDATA[Tanjung, Irvan]]>;
<![CDATA[Ilyas, Rid]]>;
<![CDATA[Melina, Melina]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.750
<![CDATA[A paraphrase is a sentence that is re-expressed with a different word arrangement without changing its meaning (semantics). To find out the semantic proximity to the pair of citation sentences in the form of paraphrases, a computational model is needed. In doing classification sometimes appears a problem called Imbalance Class, which is a situation in which the distribution of data of each class is uneven. There are class groups that have less data (minorities) and class groups that have more data (majority). Any unbalanced real data can affect and decrease the performance of classification methods. One way to deal with it is using the SMOTE method, which is an over-sampling method that generates synthesis data derived from data replication in the minority class as much as data in the majority class. The study applied SMOTE in the classification of semantic proximity of citation pairs, used Word2Vec to convert words into vectors, and used the BiLSTM model for the learning process. The research was conducted through 8 different scenarios in terms of the data used, the selection of learning models, and the influence of SMOTE. The results showed that scenarios using previous research data with BiLSTM and SMOTE models provided the best accuracy and performance.]]>
<![CDATA[Comparative Analysis of Altman and Grover's Methods in Predicting Bankruptcy Using the McNemar Test (Case Study: Vehicle Insurance Company in Indonesia) ]]>
<![CDATA[Siahaan, Roy Donald Pangeran]]>;
<![CDATA[Rizqullah, Muhammad Rifan Marsa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.823
<![CDATA[Vehicle insurance is an important component of automotive financing and consumer protection, which includes various forms of protection that protect the vehicle and its owner. Predicting the bankruptcy of a vehicle insurance company is also very important for vehicle insurance companies to be able to identify potential financial problems as early as possible and take the necessary corrective actions. The Altman and Grover model can be a way to analyze bankruptcy in company. In this study, PT. Asuransi Astra Buana, PT. Allianz Utama Indonesia, PT. Sinar Mas Insurance, and PT. BCA Insurance are used as the analyzed company. The McNemar Test conducted in this study shows that the two methods do not have significant differences in result, so the two methods will relatively have same results.]]>
<![CDATA[Sentiment Analysis of Maxim App User Reviews in Indonesia Using Machine Learning Model Performance Comparison ]]>
<![CDATA[Saefullah, Rifki]]>;
<![CDATA[Yohandoko, Setyo Luthfi Okta]]>;
<![CDATA[Prabowo, Agung]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.762
<![CDATA[User reviews can vary widely in language and writing style, which can make accurate sentiment modeling difficult. Selecting the right machine learning model and comparing performance between models can be challenging, given that each model has its own strengths and weaknesses. The method used involved data collection by scraping 5000 reviews from the Google Play Store, followed by data pre-processing including data cleaning, tokenization, stemming, and feature engineering using TF-IDF. The data was divided into training (70%) and testing (30%) sets, with the SMOTE oversampling technique applied to address class imbalance. Three machine learning models were used: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The results showed that the majority of reviews were positive, with a high average app rating. Word cloud analysis revealed that “service”, “driver”, “price”, and “time” were the most frequently discussed aspects in the reviews. In terms of model performance, SVM performed the best with an accuracy of 91.3%, followed by Random Forest (89%) and Naive Bayes (78%). Maxim was generally well received by users in Indonesia, with the majority of reviews being positive. The SVM model proved to be the most effective in classifying review sentiment, outperforming other models in accuracy and precision.]]>
<![CDATA[Actuarial Pension Fund Using the Projected Unit Credit (PUC) Method: Case Study at PT Taspen Cirebon Branch Office ]]>
<![CDATA[Amalia, Hana Safrina]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.745
<![CDATA[The pension fund program is a program held by the government to ensure the welfare of Civil Servants (PNS) in retirement as old-age security. The pension program for civil servants is managed by a pension fund, PT Taspen (Persero). Actuarial calculations of pension funds need to be carried out to determine the amount of normal contributions and actuarial liabilities that must be paid by pension plan participants and companies. The actuarial calculation of pension funds used by PT Taspen in managing civil servant pension funds is the Accrued Benefit Cost which determines in advance the benefits that will be obtained by participants. The Projected Unit Credit (PUC) method is one part of the Accrued Benefit Cost. This study aims to determine normal contributions and actuarial liabilities using the Projected Unit Credit (PUC) method for civil servant pension program participants of PT Taspen (Persero) Cirebon Branch Office. The calculation results show that the PUC method provides a more accurate calculation of the estimated normal contributions and actuarial liabilities of the company. This study is expected to be a reference for other companies in managing employee pension funds using an actuarial approach.]]>
<![CDATA[Feasibility Analysis of Establishing a Gudeg Jogja Business Using the Net Present Value (NPV) Method in the City of Jakarta ]]>
<![CDATA[Putri, Mutiara Silvia]]>;
<![CDATA[Trianandra, Fiona]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.831
<![CDATA[This research aims to analyze the feasibility of establishing a Jogja gudeg business in the city of Jakarta using the Net Present Value (NPV) method. Gudeg, as a typical Yogyakarta culinary specialty, has quite large market potential in Jakarta considering the high public interest in traditional and unique foods. This research will examine various aspects, including technical analysis, financial analysis, and sensitivity analysis. Financial analysis will focus on NPV calculations to measure the added value of investments in the long term. It is hoped that the results of the research will provide a clear picture of the potential success of the Jogja gudeg business in Jakarta and become a reference for prospective entrepreneurs who are interested in the culinary business.]]>
<![CDATA[Risk Analysis Using Poisson-Pareto Models to Estimate Reserve Funds for Catastrophic Diseases in National Health Insurance ]]>
<![CDATA[Yohandoko, Setyo Luthfi Okta]]>;
<![CDATA[Pangestika, Almira Ajeng]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.818
<![CDATA[Catastrophic diseases such as heart disease, cancer, stroke, and kidney failure pose significant financial burdens on national health insurance systems due to their high treatment costs and frequency. This study utilizes the Poisson-Pareto model to analyze aggregate claims and determine premium loading for these diseases, ensuring the financial sustainability of the National Health Insurance program. Using secondary data from 2018 to 2023, we estimate the parameters for frequency and severity distributions, calculate the expected aggregate claims, and derive the required premium loading at various confidence levels. The results show that heart disease accounts for the highest reserve fund allocation, while kidney failure requires the lowest. These findings emphasize the importance of preparing sufficient reserve funds to manage financial risks associated with catastrophic diseases. The proposed approach provides a robust framework for national health insurance providers to maintain financial stability and optimize resource allocation for high-cost diseases.]]>
<![CDATA[Mathematical Modeling of Pulling Force in Tug of War Competitions: A Tribute to Indonesia's Independence Anniversary ]]>
<![CDATA[Pirdaus, Dede Irman]]>;
<![CDATA[Laksito, Grida Saktian]]>;
<![CDATA[Hidayana, Rizki Apriva]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.754
<![CDATA[Tug of war is a folk game that uses a mining tool (rope). How to play a team with 2 teams facing each other. Each team consists of 3 or more people, who face each other holding the mine to be pulled. This tug-of-war competition activity is to train body strength, teamwork and cohesiveness. Once the second mark on the rope from the center red mark crosses the center line, the team that pulls the rope to their area wins the game. In this tug of war game there are many styles, including: Frictional Force, Tensile Force, Gravitational Force, and Muscular Force. This paper aims to study the physical forces of tug of war with a mathematical model based on the physical phenomena that exist in the game of tug of war. This model is created by considering tug of war as two objects connected by a rope. The analysis is done by considering the forces acting in the model. The results show that if after being pulled with a force F, the object moves to the right with an acceleration of a, then the acceleration of the object is based on the equation of motion according to Newton's law.]]>
