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[Application of Structural Equations Modeling Partial Least Square at the Comparation of the Niveau of Responsibility From Cs and Digics ]]>
<![CDATA[Pradana, Visca Nadia]]>;
<![CDATA[Sirait, Haposan]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i1.604
<![CDATA[Banking is an institution that plays a role in increasing economic development and also increasing equitable development. People who are serving users will be more selective in choosing banks so that many banks strive to be superior and more satisfying than other banks. Customer satisfaction can be seen from the role of CS and DigiCS. Customer Service ( CS ) is all actions intended to meet needs and activities by providing services so that each customer's needs are met. Digital Customer Service (DigiCS) is BNI digital banking automation that provides customers with immediate experience when carrying out digital transactions at BNI . The aim of this research is to determine the factors that influence the level of CS and DigiCS customer satisfaction with several variables, namely product quality ( ), service quality ( ), time ( ), convenience/efficiency ( ), and customer satisfaction (Y). The method used in this research is structural equation modeling partial least squares with the help of Microsoft Excel and SmartPLS software with the application of SEM - PLS to analyze the relationship between endogenous latent variables and exogenous latent variables. The results of this research are that for CS customer satisfaction it is found that only the exogenous variable product quality ( ) with its influences indicators customer satisfaction (Y) while for DigiCS customer satisfaction the results are that only the exogenous variable product quality ( ) and the exogenous variable convenience/efficiency ( ) with indicators that influence customer satisfaction (Y).]]>
<![CDATA[The Effect of Company Size and Working Capital on Net Income (An Empirical Study of Manufacturing Sector Companies in the Consumer Goods Industry, Home Appliances Sub-Sector Ladder Registered on the IDX from 2015 to 2020) ]]>
<![CDATA[Priatna, Husaeri]]>;
<![CDATA[Anggraeni, Iseu]]>;
<![CDATA[Iqbal, Muhammad]]>;
<![CDATA[Sofwan, Syifa Vidya]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i1.605
<![CDATA[This study examines how working capital and firm size affect net profit (Empirical Study of Manufacturing Companies in the Consumer Goods Industry Sector, Household Appliances Sub Sector Listed on the IDX for the 2015 – 2020 period). Multiple linear regression analysis was used to determine the effect of two independent variables on one dependent variable. The population in this study is financial reports published by Manufacturing Companies listed on the IDX in the Consumer Goods Industry Sector, Household Appliances Sub-Sector. The sample was taken for six years, from 2015 to 2020, using the Financial Position Report and Profit and Loss Reports to obtain data, Company Size, Working Capital, and Net Income. According to the study's findings, firm size and working capital both have positive and substantial effects on net profit, with the latter having an influence on net profit that is both positive and significant. Other factors that influence Net Profit but are not analysed include the 83.3% outcome of the Coefficient of Determination and the remaining 16.7%. ]]>
<![CDATA[A Scoping Review of Green Supply Chain and Company Performance ]]>
<![CDATA[Ningrum, Endah Prawesti]]>;
<![CDATA[Nugroho, Arissetyanto]]>;
<![CDATA[Darmansyah, Darmansyah]]>;
<![CDATA[Ahmar, Nurmala]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i1.608
<![CDATA[Environmental pollution is a serious problem that can cause the extinction of living things on earth if it is not addressed immediately. Implementing a green supply chain is one form of company attention to answer these demands. This research aims to analyze the influence of green supply chains on company performance. This research was carried out using the literature review method by reviewing various previous studies contained in various electronic journal or literature search databases. The results of this research found that the green supply chain is an important strategy for achieving sustainable development for companies. The biggest driving factors for implementing a green supply chain usually come from outside the company, namely government regulations and environmentally conscious customers. Companies must also evaluate product design and production techniques and presentation in order to produce products that are more environmentally friendly.]]>
<![CDATA[The Comparison of Investment Portfolio Optimization Result of Mean-Variance Model Using Lagrange Multiplier and Genetic Algorithm ]]>
<![CDATA[Syahla, Raynita]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Napitupulu, Herlina]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i1.611
<![CDATA[Investment portfolio optimization is carried out to find the optimal combination of each stock with the aim of maximizing returns while minimizing risk by diversification. However, the problem is how much proportion of funds should be invested in order to obtain the minimum risk. One approach that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The purpose of this study is to compare the results of the Mean-Variance model investment portfolio optimization using Lagrange Multiplier method and Genetic Algorithm. The data used are stocks that are members of the LQ45 index for the period February 2020-July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL stocks. The optimal portfolio generated by the Lagrange Multiplier method has a risk of 0.000606 and a return of 0.000726. Meanwhile, using the Genetic Algorithm resulted in a risk of 0.000455 and a return of 0.000471. Thus, the Genetic Algorithm method is more suitable for investors who prioritize lower risk. Meanwhile, the Lagrange Multiplier method produces a relatively higher risk, making it less suitable for investors who expect a small risk. ]]>
<![CDATA[Optimal Portfolio Using Roy’s Safety-First Method on Primary Consumer Goods Sector Stocks ]]>
<![CDATA[Dianti, Estu Putri]]>;
<![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) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i1.641
<![CDATA[Before carrying out investment activities, investors need to form an optimal investment portfolio. This study aims to form an optimal portfolio in primary consumer goods sector stocks that sell the basic needs of the community so that stocks in the sector tend to be stable. The method used in forming the optimal portfolio is Roy's Safety-first method. The portfolio formed produces 6 combinations of stocks consisting of WIIM, DSNG, MRAT, CAMP, SIMP, and MBTO stocks respectively with a proportion of funds of 44.05%, 16.38%, 18.61%, 15.06%, 4.32%, and 1.59% with an expected return portfolio of 3.10% and a portfolio risk of 1.65%.]]>
<![CDATA[Strategizing Financial Triumph: Applying Advanced Mathematical Models to Revolutionize Bond Investments in the Modern Financial Industry ]]>
<![CDATA[Pasha, Raisa Huria]]>;
<![CDATA[Seno, Nathaniela Apdie]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i2.668
<![CDATA[The importance of applying advanced mathematical models in bond investing marks a revolutionary step in the modern financial industry, enabling more scalable and adaptive strategies to achieve financial success. The purpose of this talk is to explore and detail the role of advanced mathematical models in changing the bond investment paradigm. The discussion aims to highlight the crucial role of advanced mathematical models in changing the bond investment paradigm, providing a deeper understanding of the optimal potential and risks involved, explaining how this approach can optimize financial outcomes through more detailed analysis. The application of mathematical models involves the use of sophisticated algorithms and statistical analysis to identify optimal investment opportunities. These steps include the use of advanced financial math formulas, such as yield to maturity and duration, to design investment strategies that are adaptive and responsive to bond market dynamics. The application of mathematical models results in a deeper understanding of the bond market, allowing investors to respond quickly to changing market conditions. Thus, the investment strategy formed by this approach can not only improve investment returns, but also reduce the risks that investors may face. The application of advanced mathematical models in bond investing opens the door to smarter and more informed decision-making. By combining data and mathematical analysis, investors can maximize potential investment returns and manage risks more effectively.]]>
<![CDATA[Mathematical Model Analysis of Mosaik Disease Spread on Jatropha Plants: Article Review ]]>
<![CDATA[Rahmani, Ayun Sri]]>;
<![CDATA[Subiyanto, Subiyanto]]>;
<![CDATA[Supian, Sudradjat]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i2.669
<![CDATA[Mosaic disease is one of the plant diseases that can be detrimental and cause crop failure, the disease is caused by Begomovirus. Begomovirus is spread by whitefly vectors. The whitefly as a vector can infect healthy plants because once the whitefly is infected, the whitefly body will forever contain the disease. Therefore, we need a mathematical model to prevent the spread of mosaic disease on Jatropha plants and make a strategy to prevent mosaic disease with optimal control and other factors. In this study, mathematical modeling of the spread of jatropha mosaic disease will be discussed, with the addition of various compartments, parameters, and optimal control. Several strategies that can be used to prevent mosaic disease in Jatropha are adding effect awareness, delay, insecticides, interventions, natural predators, yellow stick, rouging, and a combination of all strategies.]]>
<![CDATA[Prediction of Motor Vehicle Insurance Claims Using ARMA-GARCH and ARIMA-GARCH Models ]]>
<![CDATA[Maraya, Nisrina Salsabila]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i2.672
<![CDATA[Motorized vehicles are one of the means of transportation used by Indonesian people. As of 2021, the Central Statistics Agency (BPS) recorded the growth of motorized vehicles in Indonesia reaching 141,992,573 vehicles. Lack of control over the number of motorized vehicles results in losses for various parties, such as accidents, damage and other unwanted losses. The size of insurance claims has the potential to fluctuate, because it is influenced by several factors, such as policy changes, market conditions and economic conditions. This research aims to predict the size of motor vehicle insurance claims using the ARMA-GARCH model which is used to predict the size of vehicle insurance claims by dealing with non-stationarity and heteroscedasticity in time series data. Based on research, the best model obtained is the ARMA(3,3)-GARCH(1,0) model which produces nine significant parameters. Meanwhile, based on the MAPE value, it shows that the ARMA(3,3)-GARCH(1,0) model is quite accurate. The results of this research can be taken into consideration in predicting the size of insurance claims in the future.]]>
<![CDATA[Based Stock Valuation Analysis on Fuzzy Logic for Investment Selection (Case Study: PT. XL Axiata Tbk. and PT. Telkom Indonesia Tbk.) ]]>
<![CDATA[Audina, Maudy Afifah]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i2.673
<![CDATA[The stock value of a company fluctuates with capital market conditions, requiring investors to consider various factors for precise investment decisions. Stock valuation determines the fair price of a company's stock, guiding buying and selling transactions. This research uses Discounted Cash Flow (DCF), Price to Earnings (P/E), and Enterprise Value to EBITDA (EV/EBITDA) to ascertain fair stock prices, integrating results with Mamdani fuzzy logic to determine investment weights. The result of this research is that both EXCL and TLKM hold significant weight in the investment portfolio with TLKM has slightly higher stock weight than EXCL. This suggests TLKM offers more potential for profitable future investments. Investors can use these results in portfolio management for investment selection]]>
<![CDATA[Implementation of Bidirectional Long Short Term Memory (BiLSTM) Algorithm with Embedded Emoji Sentiment Analysis of Covid 19 Anxiety Level and Socio Economic Community ]]>
<![CDATA[Marcelina, Jenie]]>;
<![CDATA[Tosida, Eneng Tita]]>;
<![CDATA[Aryani, Adriana Sari]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46336/ijqrm.v5i2.682
<![CDATA[The COVID-19 pandemic has presented multidimensional challenges in Indonesia, significantly affecting social, economic, and public health at the level of anxiety. Public anxiety related to the pandemic can be reflected in online media, especially Twitter, which is the main channel for information sharing and emotional expression. This study aims to understand the level of public anxiety in relation to the aftermath of the COVID-19 pandemic by using a classification method. Classification is carried out using the Knowledge Discovery in Database method with the Bidirectional LSTM algorithm and emoji embedding sentiment analysis, and K-Fold Cross Validation testing is also carried out with various optimizers. The final result of the best accuracy rate obtained was 98.08%. This shows that the classification model created is good.]]>
