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
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<![CDATA[Enhancing Email Client Security with HMAC and PGP Integration to Mitigate Cyberattack Risks ]]>
<![CDATA[Oktaviani, Ayu Nur]]>;
<![CDATA[Hadiana, Asep Id]]>;
<![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.v5i3.758
<![CDATA[The rapid advancement of technology in the modern era has significantly increased the risk of data breaches and misuse, particularly in email communications. Ensuring data privacy and security is crucial to preventing information theft and mitigating cyberattack risks. This research focuses on enhancing email client security through the integration of Hash-Based Message Authentication Code (HMAC) and Pretty Good Privacy (PGP). HMAC is employed as a message authentication mechanism to ensure the integrity and authenticity of email messages, while PGP is utilized to generate public and private key pairs, enabling secure encryption and decryption processes. By integrating these two security methods into the email client system, we aim to enhance its resilience against cyber threats. The system's effectiveness was evaluated through black-box testing, demonstrating its capability to secure the email delivery process. Additionally, an analysis of key randomness using the entropy method revealed a maximum value of 6 bits, indicating a relatively high level of randomness and further strengthening the encryption process. The results of this study indicate that the combined use of HMAC and PGP provides a robust security solution for enhancing email client security and mitigating potential cyberattack risks.]]>
<![CDATA[Mean-Variance Portfolio Optimisation Model for Comparison of Stock Portfolio Composition on the American Stock Exchange before and after the Boycott of Companies Supporting Israel (Case Study: AAPL, SBUX, AMZN, GOOGL, MCD) ]]>
<![CDATA[Fasa, Rayyan Al Muddatstsir]]>;
<![CDATA[Sukono, Sukono]]>;
<![CDATA[Salih, Yasir]]>
<![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.v5i3.744
<![CDATA[The background of this research is related to the boycott of companies that support Israel, which affects the composition of stock portfolios on the American Stock Exchange. The focus of this research is on key companies such as Apple (AAPL), Starbucks (SBUX), Amazon (AMZN), Google (GOOGL), and McDonald's (MCD). The problem to be solved is the identification of changes in optimal asset allocation in investment portfolios before and after the boycott. Using a mean-variance portfolio optimization model, historical stock price data is analyzed to model the transformation of portfolio composition as well as the associated risk level. The purpose of this study is to provide an in-depth understanding of the impact of the boycott on the investment portfolio structure of related companies on the American Stock Exchange. The result of this research is that there is a change in the allocation of assets held against stocks before the boycott and after the boycott. This research is expected to provide useful insights for investors, financial analysts, and other stakeholders in managing their investment portfolios, especially in anticipating and adjusting investment strategies amid dynamic changes in the stock market.]]>
<![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) ]]>
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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[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) ]]>
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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) ]]>
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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.]]>
