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[Determination of Dominant Factors Affecting Lung Cancer Patients Using Principal Component Analysis (PCA) ]]>
<![CDATA[Amal, Moh Alfi]]>;
<![CDATA[Suhaimi, Nurnisaa binti Abdullah]]>;
<![CDATA[Yasmin, Arla Aglia]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i3.747
<![CDATA[The diagnosis of lung cancer is one of the most pressing health issues as the disease is often only detected at an advanced stage, leading to a poor prognosis for patients. Therefore, in an effort to detect, prevent, and manage the disease more effectively, this study utilized screening variables. Screening is an important endeavor in the early detection of diseases or abnormalities that are not yet clinically apparent using various tests, examinations, or procedures. The use of screening variables is very important in the early detection process because it can help in this study to understand the risk factors and causes of disease. The purpose of this study is to determine the dominant factors affecting people with lung cancer using Principal Component Analysis (PCA). Based on the results of the study, it was found that there are 20 dominant screening variables that have a considerable correlation to the formation of early detection of lung cancer with a total proportion of covariance variance of 100% when, the total variance obtained from the 20 screening variables is 100%. The final PCA results show that the factor loading values are used to determine which variables are most influential by comparing the magnitude of the correlation. As a result, the main factor causing lung cancer was Fatigue which had a factor loading of 7.87%, followed by the variables Age and Alcohol use with a factor loading of 6.02%. Other variables also showed certain factor loadings that indicated the cause of lung cancer. These findings are very important in efforts to improve early detection and management of lung cancer through more effective and targeted screening.]]>
<![CDATA[The Effect of Double Date Discounts on Sales Levels In E-Commerce Shopee (Case Study on Students of Padjadjaran University in Jatinangor) ]]>
<![CDATA[Wahid, Alim Jaizul]]>;
<![CDATA[Millantika, Salwa Cendikia]]>;
<![CDATA[Supriatna, Asep Kuswandi]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i4.835
<![CDATA[This study aims to analyze the impact of double date sale discounts on sales levels on the Shopee e-commerce platform, focusing on students from Universitas Padjadjaran in Jatinangor, who are primarily from the millennial and Generation Z cohorts. The method used is simple linear regression, linking discount variables to sales. Additionally, the study conducts classical assumption testing to ensure the models validity and sensitivity analysis to assess the effect of parameter changes on the predicted outcomes. The results show that double date sale discounts significantly influence sales, with the double date sale coefficient (eta_1) being highly sensitive to changes. The regression model yields a low MSE, indicating good prediction accuracy. While changes in the intercept (eta_0) also affect the predictions, the impact is smaller compared to changes in the double date sale coefficient.]]>
<![CDATA[Bankruptcy Risk Analysis in Manufacturing Companies in Indonesia using the Conan & Holder Model, J-UK Model, and Taffler Model ]]>
<![CDATA[Djonaputra, Khalifa Adli]]>;
<![CDATA[Nadira, Rana Syifa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i4.821
<![CDATA[This study aims to analyze the bankruptcy risk of manufacturing companies in Indonesia using three different bankruptcy prediction models: the Conan & Holder Model, the J-UK Model, and the Taffler Model. To predict the bankruptcy risk with each model, historical financial data from several manufacturing companies listed with the Financial Services Authority (OJK) is used. This research concludes that the combination of these three models provides valuable insights in efforts to enhance the resilience and stability of the manufacturing sector in Indonesia by offering a more comprehensive approach to identifying and managing bankruptcy risks in manufacturing companies. This research is expected to contribute to the development of more effective risk management strategies for the manufacturing industry in Indonesia.]]>
<![CDATA[Model for Determining Earth's Gravitational Acceleration on a Mathematical Pendulum ]]>
<![CDATA[Azahra, Astrid Sulistya]]>;
<![CDATA[Yuningsih, Siti Hadiaty]]>;
<![CDATA[Kalfin, Kalfin]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i3.760
<![CDATA[Gravity is an accelerating property of the earth that causes objects to fall freely. The acceleration of gravity is not the same at every place on the Earth's surface. To measure the Earth's gravity (small g), scientists can use various techniques, such as dropping a mass from a certain height and measuring the time it takes to fall to the ground or using a mathematical pendulum to measure the period of oscillation and use it to calculate the acceleration due to gravity. In this paper, a study of the mathematical pendulum in the measurement of the Earth's gravitational acceleration is conducted, and the measurement experiment is illustrated. Method To measure the length of the pendulum, you must have a ruler, meter stick, or tape measure. At the top end of the string, start the measurement at the point where the string rotates out of place. Then, measure up to the center of the pendulum, which is the object hanging on the string. From the results of this study, it can be concluded that the value of the period of a pendulum is affected by several factors, including the length of the rope used and the angle of initial deviation, while the factors that do not affect the period are the mass and diameter of the pendulum.]]>
<![CDATA[The Development of Atomic Structures by Dalton, Thomson Rutherford and Bohr, and their Mathematical Equations ]]>
<![CDATA[Suhaimi, Nurnisaa binti Abdullah]]>;
<![CDATA[Cahyandari, Rini]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i3.759
<![CDATA[Thomson's atom is a solid ball or billiard ball with a positive charge that contains several negatively charged particles or electrons. These electrons will be spread on the ball like raisins on bread. The main difference between Thomson's and Rutherford's atomic models is that Thomson's model does not contain information about the atomic nucleus, while Rutherford's model does. The theory of atomic structure helps scientists understand why elements behave in certain ways in chemical reactions. For example, electron configuration determines how elements bond and form compounds. In this paper, a literature review was conducted on the development of Thomson's atomic structure model. The study method was carried out to identify elements based on their atomic number, determine their reactivity based on the number of valence electrons, and understand how atoms unite to form molecules through chemical bonds. The results of the study, by studying atomic theory, can find out about the chemical and physical properties, as well as the uses of particles or substances that exist around the universe.]]>
<![CDATA[The Application of Compound Interest in Investment Portfolios ]]>
<![CDATA[Janardana, Komang]]>;
<![CDATA[Wiriandi, Daffa Ibrahim]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i4.829
<![CDATA[Effective long-term investment requires a well-structured strategy supported by detailed analysis. The compound interest model serves as a pivotal tool in assessing potential returns on investments by illustrating how interest accumulates on both the initial capital and previously accrued interest. This study delves into the application of compound interest within investment portfolios, aiming to elucidate its impact on long-term growth trajectories. By investigating various factors, such as investment duration and compounding frequency, the research highlights the intricate mechanisms driving investment expansion. A robust understanding of these elements is crucial for making informed financial decisions. The insights gained from this research are intended to equip investors and financial advisors with practical strategies for optimizing portfolio performance and achieving superior investment results, ultimately contributing to the advancement of more sustainable long-term investment practices.]]>
<![CDATA[Implementation of Ruin Probability Model in Life Insurance Risk Management ]]>
<![CDATA[Lianingsih, Nestia]]>;
<![CDATA[Hidayana, Rizki Apriva]]>;
<![CDATA[Saputra, Moch Panji Agung]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i4.816
<![CDATA[This study examines the implementation of the ruin probability model in risk management in life insurance companies. The main focus of this study is to evaluate how factors such as initial surplus, premium revenue level, and claim frequency affect the ruin probability of insurance companies. Using the collective risk model approach and relevant claim distribution, this study develops two methods to calculate the ruin probability: an analytical approach and a Monte Carlo simulation. The simulation results show that increasing the initial surplus and premium level significantly reduces the ruin risk, while increasing the claim frequency increases the ruin probability. In addition, the gamma claim distribution is more suitable for modeling claims in life insurance than the exponential distribution. Model validation is carried out by comparing the prediction results with historical data of insurance companies, which shows a high level of accuracy. This study provides important insights for insurance companies in designing more effective and optimal risk management strategies.]]>
<![CDATA[Determination of Monthly Term Health Insurance Premiums for Individuals Based on Gender ]]>
<![CDATA[Siahaan, Roy Donald Pangeran]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v6i1.866
<![CDATA[Health is one of the important aspects of human life, and protection against health risks is a necessity for every society. Health insurance is a solution in providing protection against financial risks from health. In its implementation, determining premiums is an important factor for insurance companies in order to cover claims from policyholders. Premiums are money paid by policyholders to insurance companies in order to receive benefits in the future. This study aims to determine the monthly premium for term health insurance which adjusted for each gender using actuarial approach. The premium is determined using the 2023 Indonesian Mortality Table and the Indonesian Morbidity Table I "Critical Illness". Based on this study, it was found that the value of the monthly term health insurance premium will increase if the policyholder's entry age and the insurance contract period increase. This study also found that the premium values for men were greater than the premium and reserve values for women if the policy entry age of the man or woman was over 30 years, in addition, the premium and reserve values for women were greater.]]>
<![CDATA[Investment Portfolio Optimization on Technology Sector Stocks Using Mean-Variance Model with Asset-Liability Based on ARIMA-GARCH Approach ]]>
<![CDATA[Bisyarah, Sania]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v6i1.864
<![CDATA[In this era of rapid technological advancement, various sectors are experiencing changes, one of which is investment. Investors are starting to turn their attention to technology sector stocks as new investment targets. However, investments are inherently linked to return and risk levels and stock prices can be highly volatile. Therefore, forming an optimal investment portfolio is very important to achieve a balance between return and risk. In addition, coping with volatile stocks is also very important. The ARIMA-GARCH time series model is a method that can be used to deal with such volatility. A popular strategy for portfolio optimization is to use the Mean-Variance model, also known as the Markowitz model. This study aims to form an optimal portfolio consisting of five technology sector stocks in Indonesia with the codes AXIO, DIVA, EDGE, MCAS, and CASH using the Mean-Variance model with assets-liabilities equipped with the ARIMA-GARCH approach. Based on the results of the study, the optimal portfolio is obtained with the composition of each weight is 23.16% of the capital allocated to AXIO; 2.95% for DIVA; 56.48% for EDGE; 6.36% for MCAS; and 11.05% for CASH. The weight allocation composition can generate a portfolio return of 0.0066 and a variance (risk) return of 0.0082.]]>
<![CDATA[Modeling Queue Length at The Toll Gate Using Promodel Before and After Ramp-Off Construction ]]>
<![CDATA[Hafizi, Muhamad]]>;
<![CDATA[Hafiz, Syauqi Abyan]]>;
<![CDATA[Sugiharto, Bambang]]>;
<![CDATA[Tosida, Eneng Tita]]>;
<![CDATA[Bon, Abdul Thalib Bin]]>;
<![CDATA[Sugara, Victor Ilyas]]>;
<![CDATA[Subandi, Kotim]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v6i1.905
<![CDATA[In everyday life, queues often occur. Waiting at the counter to get train or movie tickets, at the toll gate, at the bank, at the supermarket, and in other situations that we often encounter Queues occur when the need for services exceeds the capacity or capacity of the service facility. As a result, users of the facility cannot get immediate service due to the busyness of the service. The Amplas Toll Gate queue is the object of this research. The Amplas Toll Gate is one of the densest toll gates that is heavily traveled by vehicles both entering and exiting. This makes it often seen a fairly long queue, especially during peak hours in the late afternoon to evening. The Medan City Government built an off ramp at the Amplas flyover in 2016. This off ramp leads directly to the Amplas toll gate. The vehicle arrival rate increases along with the queue length because vehicles can arrive faster to the toll gate. This study aims to calculate the queue length at the Amplas toll gate before and after the construction of the ramp off. Data is obtained by recording the volume of vehicles at the research location. With an average service time of 7 seconds, the queuing method produces a queue length of 11.98 meters, while the results using Pro Model software are 11.98 meters. In addition, the queue length after the construction of the ramp off decreased to 6.67 meters from before the construction of the ramp off. Promodel is a windows-based simulation software used to simulate and analyze a system.]]>
