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International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
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
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Vol. 5 No. 1 (2024)" : 15 Documents clear
Application of Structural Equations Modeling Partial Least Square at the Comparation of the Niveau of Responsibility From Cs and Digics Pradana, Visca Nadia; Sirait, Haposan
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.604

Abstract

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).
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) Priatna, Husaeri; Anggraeni, Iseu; Iqbal, Muhammad; Sofwan, Syifa Vidya
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.605

Abstract

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%. 
A Scoping Review of Green Supply Chain and Company Performance Ningrum, Endah Prawesti; Nugroho, Arissetyanto; Darmansyah, Darmansyah; Ahmar, Nurmala
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.608

Abstract

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.
The Comparison of Investment Portfolio Optimization Result of Mean-Variance Model Using Lagrange Multiplier and Genetic Algorithm Syahla, Raynita; Susanti, Dwi; Napitupulu, Herlina
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.611

Abstract

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. 
Optimal Portfolio Using Roy’s Safety-First Method on Primary Consumer Goods Sector Stocks Dianti, Estu Putri; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.641

Abstract

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%.

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