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[Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application ]]>
<![CDATA[Sadiah, Halimah Tus]]>;
<![CDATA[Purnama, Delta Hadi]]>;
<![CDATA[Ishlah, Muhamad Saad Nurul]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.552
<![CDATA[This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form]]>
<![CDATA[Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques ]]>
<![CDATA[Yadav, Nikesh]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.567
<![CDATA[Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices.]]>
<![CDATA[Calculation of Term Life Insurance Premium Reserves with Fackler Method and Canadian Method ]]>
<![CDATA[Zakirah, Khalilah Razanah]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Riaman, Riaman]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.589
<![CDATA[Every individual around the world goes through the life cycle of birth and continues their journey with unique experiences. The uncertainty of the future, which includes both happiness and calamity, is a universal aspect of human life. Life risks, such as illness and death, are an unavoidable reality for every individual in this world. Life insurance is one of the solutions to manage these risks, with term life insurance being one of the options. The focus of this research lies on term life insurance, with the aim of calculating premium reserves using the Fackler and Canadian methods. This research is concerned with the process of calculating premium reserves, and the results show that the Fackler method produces a larger premium reserve value compared to the Canadian method. Recommendations are given to companies to use the Fackler Method in calculating term life insurance premium reserves to avoid potential losses that could occur if using the Canadian method. The choice of premium calculation method is a strategic key in effective risk management for the company.]]>
<![CDATA[Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks ]]>
<![CDATA[Wijaya, Silvia]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Riaman, Riaman]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.591
<![CDATA[Investment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%.]]>
<![CDATA[Comparison of the Zillmer Method with the Adjusted Ohio Method in Calculation of Premium Reserve Value in Dwi-Purpose Life Insurance ]]>
<![CDATA[Reisnanda, Aldino]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Riaman, Riaman]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.592
<![CDATA[Life insurance is one of protections in society by providing economic protection for insurance users who experience an adverse event. The insured who is an insurance user has an obligation to pay the premium at the time that is determined by the insurance company and the policyholder. Insurance companies need funds to fulfill claims from policyholders, so premiums that have been paid are stored in the form of premium reserves. Premium reserves need to be managed by the company properly so that the company does not experience losses. The purpose of this research is to provide information to determine the appropriate value of premium reserves in dual-life insurance. In this study, the calculation of premium reserves is done using the Zillmer Method and the adjusted Ohio Method, with the Prospective Method as the basis for the calculation. Based on the research results of premium reserve calculations in this study, both the Zillmer method and the Ohio method show premium reserve values that are directly proportional to the policyholder’s age. The premium reserve calculations also indicate that the Zillmer method and the Ohio method yield the same results when the insurance coverage period ends. However, there is a significant difference in the premium reserve calculations at the beginning of the insurance coverage period.]]>
<![CDATA[Application of Mathematical Model in Bioeconomic Analysis of Skipjack Fish in Pelabuhanratu, Sukabumi Regency, Jawa Barat ]]>
<![CDATA[Nurkasyifah, Fathimah Syifa]]>;
<![CDATA[Supriatna, Asep K.]]>;
<![CDATA[Napitupulu, Herlina]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.598
<![CDATA[Presently, sustainability has emerged as a crucial and compelling concern across diverse sectors, evolving into a long-term agenda championed by the United Nations through the implementation of the Sustainable Development Goals (SDGs). Within the SDGs, particularly under point 14 addressing life below water, emphasis is placed on ensuring sustainability in aquatic ecosystems, encompassing the fisheries sector. The concept of Maximum Sustainable Yield (MSY) holds significance in the bioeconomic analysis of fisheries, influencing decision-making processes aimed at preserving sustainability. Regrettably, several studies have identified inaccuracies in the determination of MSY, leading to instances of overfishing in various regions. Conversely, it is imperative to give due attention to Maximum Economic Yield (MEY) to ensure that economic considerations remain integral to decision-making processes. Consequently, a more comprehensive and detailed bioeconomic analysis, incorporating mathematical models, becomes essential. Among these models, the logistic growth rate model and the Gompertz growth rate model stand out as significant contributors. ]]>
<![CDATA[Risk Measurement of Investment Portfolio Using Var and Cvar from The Top 10 Traded Stocks on the IDX ]]>
<![CDATA[Suhaimi, Nurnisaa binti Abdullah]]>;
<![CDATA[Rusyn, Volodymyr]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.600
<![CDATA[Portfolio investment reflects a commitment to the allocation of funds or resources which is considered a strategic step in managing assets to achieve future profits. This research begins with a careful analysis of a portfolio consisting of the ten best stocks on the Indonesia Stock Exchange (IDX). Through in-depth processing and analysis of stock data, the dynamics of performance, risk and volatility involved in each investment commitment are revealed. The Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) methods at the 90%, 95%, and 99% confidence levels take centre stage, highlighting the potential for increased losses as confidence levels increase. In-depth analysis illustrates that CVaR, considering the extreme risks in the distribution, provides a more holistic picture than VaR. With a VaR (99%) value of IDR 84,973,959,424 and CVaR (99%) of IDR 471,795,822,064, this research provides a concrete picture of potential risks at the highest level of confidence. These results confirm that CVaR has a crucial role in identifying and measuring the potential for more significant losses, especially in the face of unexpected market uncertainty. As a guide for investment decision makers, this research forms an important basis for carefully considering the level of risk and potential return at various levels of confidence. This allows the development of smarter and more informed investment strategies.]]>
<![CDATA[Investment Portfolio Optimization in Renewable Energy Stocks in Indonesia Using Mean-Variance Risk Aversion Model ]]>
<![CDATA[Vimelia, Willen]]>;
<![CDATA[Riaman, Riaman]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.601
<![CDATA[Climate change is a phenomenon that has been occurring for quite some time. However, the increasingly felt impacts of climate change necessitate human action to mitigate these effects. One way to address this issue is by transitioning from conventional or non-renewable energy sources to renewable energy. This step undoubtedly has implications for various aspects, such as investments. Naturally, investors are beginning to turn their attention to the field of renewable energy as a new target. Investments are inherently associated with risks and returns One approach to maximizing returns is through portfolio optimization. One well-known method in portfolio optimization is the Mean-Variance method, also known as the Markowitz method, as it was first introduced by Harry Markowitz. In this research, an optimal portfolio is generated with weights of 0.1470 for ADRO; 0.1939 for MEDC; 0.2143 for ITMG and 0.4449 for RAJA. With this composition of optimal portfolio weights, the expected return is obtained at 0.002252, and the return variance is 0.000496.]]>
<![CDATA[Investment Portfolio Optimization In Infrastructure Stocks Using The Mean-VaR Risk Tolerance Model ]]>
<![CDATA[Yasmin, Arla Aglia]]>;
<![CDATA[Riaman, Riaman]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.602
<![CDATA[Infrastructure a crucial role in economic development and the achievement of Sustainable Development Goals (SDGs), with investment being a key activity supporting this. Investment involves the allocation of assets with the expectation of gaining profit with minimal risk, making the selection of optimal investment portfolios crucial for investors. Therefore, the aim of this research is to identify the optimal portfolio in infrastructure stocks using the Mean-VaR model. Through portfolio analysis, this study addresses two main issues: determining the optimal allocation for each infrastructure stock and formulating an optimal stock investment portfolio while minimizing risk and maximizing return. The methodology employed in this research is the Mean-VaR approach, which combines the advantages of Value at Risk (VaR) in risk measurement with consideration of return expectations. The findings indicate that eight infrastructure stocks meet the criteria for forming an optimal portfolio. The proportion of each stock in the optimal portfolio is as follows: ISAT (2.74%), TLKM (33.894%), JSMR (3.343%), BALI (0.102%), IPCC (5.044%), KEEN (14.792%), PTPW (25.863%), and AKRA (14.219%). The results of this study can serve as a foundation for better investment decision-making.]]>
<![CDATA[Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia) ]]>
<![CDATA[Labitta, Kirana Fara]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i1.603
<![CDATA[Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.]]>
