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[Do Phone and Internet Have Role to Promote Economic ]]>
<![CDATA[Rika Nurlela]]>;
<![CDATA[Taufiq c. Dawood]]>;
<![CDATA[Aliasuddin Aliasuddin]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.191
<![CDATA[As one of the developing countries in the world, Indonesia is very active in developing ICT. The dependence of the Indonesian people on ICT increases every year. The two ICT indicators experiencing rapid development are the telephone and the internet. This study aims to analyze the effects of fixed-line phone users, mobile phone users, and internet users on economic growth in Indonesia. The panel data used in this study is panel data from 33 provinces in Indonesia from 2011–2019. The results showed that mobile phone users and internet users have positive effects on economic growth. However, fixed-line phones have a negative and insignificant influence on economic growth. Advances in technology have shifted fixed-line phones to smartphones. The government is expected to control and direct mobile phones and the internet for productive activities to encourage economic improvement.]]>
<![CDATA[Tax Reform Effect on Local Tax Buoyancy in Indonesia ]]>
<![CDATA[Riyath Iskandar]]>;
<![CDATA[Srinita Srinita]]>;
<![CDATA[Putri Bintusy Syathi]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.193
<![CDATA[This study analyzes local tax efforts through the buoyancy rate method in 423 regions consisting of 341 Regency Governments and 82 City Governments in Indonesia for the period 2007 to 2019, using the panel data regression method with a fixed effect model. The research shows that changes in regional taxation policies with Law of Republic Indonesia Number 28 year 2009 concerning Local Taxes and Charges have a positive impact on efforts to collect Local Taxes with a significant increase in the value of the regional tax buoyancy rate. The value of the local tax buoyancy rate obtained is higher for the City Government than for the Regency Government, so it is necessary to adjust regional tax policies consistently to overcome the inequality of income realization that occurs between the Regency and City Governments in order to increase regional fiscal independence.]]>
<![CDATA[The Effect of Gender and Household Education Expenditure in Indonesia ]]>
<![CDATA[Tedy Di Oria Salam]]>;
<![CDATA[M. Shabri Abd. Majid]]>;
<![CDATA[Taufiq C Dawood]]>;
<![CDATA[Suriani Suriani]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.192
<![CDATA[This study empirically examines and analyzes the effect of gender on human capital investment in Indonesia. Using the logistic regression method and data sourced from 315,672 households in Indonesia, this study shows that the number of boys, the number of girls, the working status of the head of the household, and the highest education of the head of the household have a positive and significant impact on human capital investment in Indonesia. The results show that female household heads who work and invest in the cost of children's education are more significant than male household heads who also work. Higher the education level of the head of the household, the higher the income received and also investment for children. This research shows strong evidence of gender inequality in education spending that tends to be more towards girls. Based on the results obtained, development policies can consider gender differences in investment in labor and education. Increasing the school participation rate of women compared to men will increase the differentiation of the workforce by gender but also increase income inequality between men and women. Likewise, investment in education which tends to be more directed to women than men, will reduce income inequality.]]>
<![CDATA[Examining the Long and Short Run Effect of Young Workers on Macroeconomic Variables: An Application of Panel Autoregressive Distributed Lag Approach ]]>
<![CDATA[Rizky Wardhana]]>;
<![CDATA[Vivi Silvia]]>;
<![CDATA[M. Shabri Abd. Majid]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.180
<![CDATA[The purpose of this research is to analyze the linkage between young workers and macroeconomic variables in Indonesia through a cointegration and causality approach. Multivariate causality between these variables. Using ARDL panel regression (Auto-Regressive Distributed Lag) with data from 2005 – 2019 covering 33 provinces in Indonesia. The results showed that the variable government expenditure on education had no effect on young workers in the short and long term, the variable economic growth only had a positive and significant effect on young workers in the long term. The increase in the minimum wage has a significant negative effect on young workers in the short term, and vice versa, it has a positive and significant effect on the long term. The last variable that has an effect is the investment variable which has a negative and significant effect in the short term on young workers. The results of multivariate causality testing between the variables above have the result that young workers have a two-way causal relationship with the minimum wage and have a one-way relationship with government spending on education.]]>
<![CDATA[Fuzzy Time Series Application in Predicting the Number of Confirmation Cases of Covid-19 Patients in Indonesia ]]>
<![CDATA[Lintang Patria]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.194
<![CDATA[Forecasting is a statistical method that can use historical data patterns to predict future events. This article discusses the prediction of the number of new confirmed cases of Covid-19 patients in Indonesia. The data used is from January 1, 2021 to August 7, 2021. The methods used are Fuzzy Time Series (FTS) Chen (2014) and Cheng et al. (2008). FTS is a forecasting method that uses rules and logic on fuzzy sets. The level of prediction accuracy is then calculated based on the Mean Absolute Percentage Error (MAPE) value. The MAPE values of these two methods are then compared to know which method is more suitable in this case study. The results showed that FTS Chen produced an accuracy of 12.75% and FTS Cheng produced an accuracy of 14.27%. The results of this study indicate that FTS Chen and FTS Cheng produce good accuracy and can be used to predict new confirmed cases of Covid 19 sufferers in Indonesia.]]>
<![CDATA[IDX30 Stocks Clustering with K-Means Algorithm based on Expected Return and Value at Risk ]]>
<![CDATA[Ahmad Fawaid Ridwan]]>;
<![CDATA[Subiyanto Subiyanto]]>;
<![CDATA[Sudradjat Supian]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 2, No 4 (2021) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v2i4.157
<![CDATA[Stocks are one of the investment instruments available in the capital market. Several indices show the characteristics of stocks listed on the Indonesia Stock Exchange. IDX30 is one of several indications that show the combined stocks are stocks with large market capitalization, high liquidity, and good fundamentals. The selection of assets to be allocated in the portfolio is an important factor in investing where the purpose of investing is to maximize returns and minimize risk. This study aims to classify stocks that have certain characteristics based on the expected return and value at risk of the stocks incorporated in IDX30 with a clustering algorithm. The clustering algorithm used is the K-Means algorithm. K-Means is a non-hierarchical clustering algorithm by groups each object based on its proximity to the cluster center. The method used in this research is a clustering simulation study using the K-Means algorithm on IDX30 stock data. By identifying the characteristics of the stock based on the characteristics of the cluster formed, it is hoped that it can be considered in choosing the assets to be used in the formation of an optimal portfolio.]]>
