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
373 Documents
<![CDATA[The Effect of the Agriculture Sector on Poverty in Aceh Province ]]>
<![CDATA[Ridho Fatwa]]>;
<![CDATA[Srinita Srinita]]>;
<![CDATA[Muhammad Abrar]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 2 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i2.275
<![CDATA[This study aims to analyze the influence of the agricultural sector on poverty in Aceh Province. In this study, the variables used in influencing the poverty level in Aceh Province are the share of Gross domestic product (GDP) in the agricultural sector, labor in the agricultural sector, agricultural land, Farmer Education and Gross Regional Domestic Product (GRDP) per capita. The regression model used in this study is the method of multiple linear regression analysis (ordinary least squares regression analysis) using panel data and a fixed effect approach (fixed effect model) to determine the effect between variables. The results of this study are based on a simultaneous test (Test F) which shows that overall, the independent variables (share of GDP in the agricultural sector, labor in the agricultural sector, agricultural land, Farmer Education and GRDP per capita together show their effect on the poverty level. The results of the study based on a partial test (t test) showed that the share of the agricultural sector GRDP and the GDP per capita variable had a negative and significant effect on poverty and agricultural sector labor had a positive and significant effect on poverty, while the variables of agricultural land and farmer education negative effect, but not significant. The value of Adjusted R-squared in this study is 0.868629. This shows that the 86.86 percent change in the dependent variable, namely the Poverty of Aceh Province, can be explained by the independent variable, namely Share of Agricultural GRDP, Agricultural Manpower, Agricultural Land, Farmer Education and Per Capita GRDP. While the remaining 13.14% is explained by other factors outside the model.]]>
<![CDATA[Analysis of Economic Growth Core and Periphery: Evidence from Aceh Province, Indonesia ]]>
<![CDATA[Risnasari Risnasari]]>;
<![CDATA[Abd. Jamal]]>;
<![CDATA[Sofyan Syahnur]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 2 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i2.277
<![CDATA[This research is to determine factors that influenced Gross Regional Development Product (GRDP) Banda Aceh and Lhokseumawe city in context relationship between core and periphery using panel data regression of 23 districts/cities in Aceh Province, Indonesia year 2010-2020. Selected independent variables in this paper are GRDP core and periphery, population, distance between core and periphery, availability of hospital, availability of university and availability of industry. Based on estimation results, all independent variabels have significant effect toward GRDP Banda Aceh and Lhokseumawe city. Variabels that found have positive effect toward GRDP Banda Aceh city are GRDP core and periphery, and distance between core and periphery. Variabels that found have negative effect toward GRDP core Banda Aceh city are population, availability of hospital, availability of university and availability of industry. Then, variabels that found have positive effect toward GRDP Lhokseumawe city are GRDP core and periphery, distance between core and periphery, and availability of university. Variabels that found have negative effect toward GRDP core Lhokseumawe city are population, availability of hospital, and availability of industry. It is hoped that this findings will provide useful information for policymakers in attempt to enhance the competitiveness of regional economy.]]>
<![CDATA[Implementing Japanese PMA Organizational Culture in Indonesia impacts Employee Job Satisfaction, Employee Performance and Employee Retention of Japanese and Indonesian Employees ]]>
<![CDATA[Martadinata Martadinata]]>;
<![CDATA[Evi Susanti]]>;
<![CDATA[Rina Anindita]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.329
<![CDATA[The expansion of Japanese parent companies worldwide has forced them to carry the organizational culture that the Japanese founders had held onto their overseas subsidiaries. The main purpose of study is examined organizational culture, employee job satisfaction, and employee performance of Japanese PMA companies in Indonesia on employee retention. Eight Japanese companies were used as the sample, where 33 Japanese employees and 222 Indonesian employees were respondents. Theory Z is used to discuss organizational culture. The questionnaire was made in Indonesian, English, and Japanese. The research model uses a tiered structure model, while to test the proposed hypothesis, the SEM Lisrel 8.8 analysis technique is used. The main finding is the organizational culture of Japanese companies in Indonesia strongly influences job satisfaction, employee performance, and employee retention. Applying Japanese corporate culture shows that Japanese and Indonesian employees understand the company's core values. Employee performance can be realized by employees being able to understand the cultural values of the organization.]]>
<![CDATA[The Effect of the Exploration and Exploitation of Oil and Gas on Indonesian Economic Growth ]]>
<![CDATA[Yassir Achmad]]>;
<![CDATA[Sofyan Syahnur]]>;
<![CDATA[Chenny Seftarita]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.342
<![CDATA[The era of globalization and accelerated economic growth, as well as various kinds of industrial and technological transformations, are currently causing or triggering very concrete environmental problems, one of which is in terms of the growth in consumption of non-renewable energy, namely oil and natural gas. Oil and gas reserves are part of the socio-economic problems in Indonesia. It is known that oil and gas reserves are spread throughout almost all aspects of Indonesia. However, the utilization of the potential reserves of oil and natural gas resources in Indonesia is still not fully optimized. So that the potential for oil and gas reserves in Indonesia still does not fully have a more significant impact on Indonesia's economic growth. This study examines the influence of oil and gas exploration and exploitation in Indonesia on economic growth in Indonesia. This study used data on Indonesia's GDP and Exploitation and Exploitation of Indonesian Oil and Gas in a time series (1996-2021). In analyzing the data, this study used multiple linear regression. The results showed that the exploration and exploitation of oil and gas have a positive and significant effect on economic growth in Indonesia. It is hoped that this study can serve as an impetus for the government in making regulations and regulations directly related to exploration and exploitation activities both upstream and downstream of oil and gas and as encouragement and motivation for governments directly involved with upstream and downstream oil and gas activities. In addition, to issue policies in the form of continuing to prioritize technological development innovations, especially in the oil and gas sector. It is also hoped that the production results obtained from oil and natural gas exploration and exploitation activities can be more optimal and impact national energy security, state revenues, and Indonesia's economic growth.]]>
<![CDATA[Investment Portfolio Optimization Model Using The Markowitz Model ]]>
<![CDATA[Emmanuel Parulian Sirait]]>;
<![CDATA[Yasir Salih]]>;
<![CDATA[Rizki Apriva Hidayana]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.344
<![CDATA[The stock portfolio is related to how someone allocates several shares in various types of investments so that the results achieve maximum profit. By implementing a diversification system or portfolio optimization on several stocks, investors can reduce the level of risk and simultaneously optimize the expected rate of return. This study aims to determine which stocks listed on the Indonesia Stock Exchange (IDX) and included in the portfolio for the 2021-2022 period are eligible to be included in the optimal portfolio and to determine the proportion of funds for each share in the formation of the optimal portfolio. The population in this study are all shares included in the Indonesia Stock Exchange (IDX) listed on the Indonesia Stock Exchange (IDX) for the 2021-2022 period. The sample of this research is five stocks that are candidate portfolios. The sampling method uses a purposive sampling method with the criteria of 5 stocks with the highest positive ratio. The population in this study was all 30 companies included in the IDX30, while the samples were five companies. Data were analyzed using a mean-variant optimization model with a research duration between May 2021 and May 2022. Based on the results of the investment portfolio optimization analysis on the 5 (five) selected stocks, this study shows that, out of 23 stocks, five stocks are eligible to enter the optimal portfolio with their respective proportions, namely PT Adaro Energy Indonesia Tbk (ADRO) 20%, PT Astra International Tbk (ASII) 26%, PT Merdeka Copper Gold Tbk (MDKA) 10%, PT XL Axiata Tbk (EXCL) 19%, PT Bukit Asam Tbk (PTBA) 25%. The portfolio of these stocks generates an expected return of 0.00217 at a risk level of 0.00022. It is hoped that this research can be helpful to add to the literature on investment optimization models, especially the concentration of Mathematics in Finance, and serve as an additional reference for further research, as well as an alternative for investors in optimizing investment portfolios.]]>
<![CDATA[Investment Portfolio Optimization with a Mean-Variance Model Without Risk-Free Assets ]]>
<![CDATA[Syifa Nur Rasikhah Daulay]]>;
<![CDATA[Nurfadhlina Abdul Halim]]>;
<![CDATA[Rizki Apriva Hidayana]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.345
<![CDATA[Investment is an allocation of money, stocks, mutual funds, or other valuable resources provided by someone at the present time and held from being used until a specified period to get a profit (return). The higher the return received, the higher the risk. This study studied the Mean-Variance investment portfolio optimization model without risk-free assets to obtain the optimum portfolio. Five shares are used, namely BMRI, AMRT, SSMS, MLPT, and ANTM. The research results obtained optimal portfolio stocks with respective weights BMRI = 0.45741; AMRT=0.17852; SSMS=0.23300; MLPT=0.08475 and ANTM=0.04632. An optimal portfolio composition produces an average return = 0.00207 and variance = 0.00020.]]>
<![CDATA[Determining the Price of Fisherman Micro Insurance Premiums Using the Aggregate Risk Model Approach in Cirebon Regency ]]>
<![CDATA[Ratih Kusumadewi]]>;
<![CDATA[Riaman Riaman]]>;
<![CDATA[Sukono Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.346
<![CDATA[Catastrophe such as hurricanes, heavy rains, and similar occurrence pose serious threats and risks to fishermen's livelihoods as well as losses from damage to their assets. Therefore, it is necessary to have special insurance to protect the fishermen's assets from financial losses due to the risks that can occur, namely Fisherman Micro Insurance. Micro-insurance is an insurance product that is intended for low-income people with features and administration that are simple, easy to obtain, economical prices and immediately in the completion of the provision of compensation. Fisherman's micro insurance guarantees assets in the form of fishing equipment in the occurrence of a risk of an accident causing damage, this insurance product protects against worries without a large premium burden. This study aims to calculate the premium price with an aggregate risk model approach. The data used is data on fisherman’s losses if they did not go to sea which obtained by surveys. The occurrence data follows the Poisson distribution, and the loss data follows the Exponential distribution. Parameter Estimation was carried out using the Maximum Likelihood Estimation. The estimation results from numbers of occurrence and the amount of losses are used to estimate the collective risk model. Estimators of the average and variance of the aggregate risk are used to determine the premium. The results of the premium selection in this study amounted to IDR 153.861.958.00. The premium amount is a collective premium which is the result of a calculation based on the standard deviation principle.]]>
<![CDATA[Company Stock Performance Analysis on IDX ESG Leaders Index Using the ARIMA-GARCH Model ]]>
<![CDATA[Hazelino Rafi Pradaswara]]>;
<![CDATA[Dwi Susanti]]>;
<![CDATA[Sukono Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i3.347
<![CDATA[Stocks are one of the most popular forms of investment. In investing stocks, it is necessary to know the movement of stock prices and the investment risks that may occur. The purpose of this study is to predict the level of risk, see the characteristics of stock returns, and whether the ESG Risk Rating makes the company's stock performance better. The models used to predict stock returns are Auto Regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticty (GARCH), and Value at Risk (VaR) is used to predict risk. Based on the research, the potential loss for Bank BCA is IDR29.800.000,00 and Bank Mandiri is IDR33.600.000,00 with the assumption that an investor invests as much as IDR1.000.000.000,00. In addition, Bank BCA has a lower ESG Risk Rating than Bank Mandiri, but has a better performance.]]>
<![CDATA[CONSTRUCTION OF MORTALITY TABLES USING UNIFORMLY DISTRIBUTION OF DEATH AND CONSTANT FORCE BASED APPROACHES IN TMI 2019 ]]>
<![CDATA[Nurul Tri Narlitasari]]>;
<![CDATA[Riri Rioke]]>;
<![CDATA[Wulan Setyani]]>;
<![CDATA[Anisa Nurbayti]]>;
<![CDATA[Agung Prabowo]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 4 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i4.356
<![CDATA[Insurance aims to protect a person from financial losses that may occur due to an unexpected event. On the determination of insurance premiums used mortality tables. However, on the mortality table contains only a round age. While an event cannot be ascertained when it occurs, it could be at the beginning of the year, in the middle, or at the end of the year. Therefore, to determine insurance premiums at an age that is not round, a mortality table that contains fractional age is needed. In this study, the mortality table used is the 2019 Indonesian Mortality Table (IMT) issued by the Indonesian Actuary Association (IAA). The methods used for determining fractional age mortality tables are the Uniform Distribution of Death (UDD) approach and the Constant Force of Mortality (CF) approach. In this study, the results of the 2019 TMI calculation were obtained for fractional ages with male and female genders using two approaches, namely the UDD and CF approaches. In both sexes, the result was obtained that the chance of death calculated using the UDD approach was smaller compared to the CF approach. The resulting graph shows that the 2019 TMI death chances with the UDD and CF approaches did not show significant differences for both men and women, so both approaches can be used to calculate the chance of death at the fractional age of TMI 2019.]]>
<![CDATA[De Moivre Law Application for the Construction of Mortality Tables Based on Indonesian Mortality Tables 2019 ]]>
<![CDATA[Elsa Anna Pratiwi]]>;
<![CDATA[Fitri Indah Ningtyas]]>;
<![CDATA[Ratna Nur Aini Kamilia]]>;
<![CDATA[Zahwa Aqila Nabilia Aqila Nabilia]]>;
<![CDATA[Agung Prabowo]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 3 No. 4 (2022): International Journal of Quantitative Research and Modeling ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v3i4.357
<![CDATA[The mortality table or often referred to as the life table is the main instrument used by actuaries in building premium and reserve structures for life insurance products, annuities, and pension programs. The mortality table provides a complete description of the mortality rate and life expectancy and shows the pattern of death of a group of people born at the same time based on the age they have reached and plays an important role as a basis for calculating the level of life expectancy in the future. This article aims to find out how to construct a mortality table with reference to the 2019 TMI for men with de Moivre's Law. In the results of the construction with de Moivre's law, the lowest value occurred at the age of 0 years, namely = 0.00900901, while the highest value occurred at the age of 110 years, namely = 1. Based on the construction of the value in the 2019 TMI for men using de Moivre's law, which is compared with the value in the 2019 TMI for men, the results tend to be the same.]]>
