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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 9 Documents
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Search results for , issue "Vol 6, No 1 (2025)" : 9 Documents clear
Determination of Monthly Term Health Insurance Premiums for Individuals Based on Gender Siahaan, Roy Donald Pangeran
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.866

Abstract

Health is one of the important aspects of human life, and protection against health risks is a necessity for every society. Health insurance is a solution in providing protection against financial risks from health. In its implementation, determining premiums is an important factor for insurance companies in order to cover claims from policyholders. Premiums are money paid by policyholders to insurance companies in order to receive benefits in the future. This study aims to determine the monthly premium for term health insurance which adjusted for each gender using actuarial approach. The premium is determined using the 2023 Indonesian Mortality Table and the Indonesian Morbidity Table I "Critical Illness". Based on this study, it was found that the value of the monthly term health insurance premium will increase if the policyholder's entry age and the insurance contract period increase. This study also found that the premium values for men were greater than the premium and reserve values for women if the policy entry age of the man or woman was over 30 years, in addition, the premium and reserve values for women were greater.
Investment Portfolio Optimization on Technology Sector Stocks Using Mean-Variance Model with Asset-Liability Based on ARIMA-GARCH Approach Bisyarah, Sania
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.864

Abstract

In this era of rapid technological advancement, various sectors are experiencing changes, one of which is investment. Investors are starting to turn their attention to technology sector stocks as new investment targets. However, investments are inherently linked to return and risk levels and stock prices can be highly volatile. Therefore, forming an optimal investment portfolio is very important to achieve a balance between return and risk. In addition, coping with volatile stocks is also very important. The ARIMA-GARCH time series model is a method that can be used to deal with such volatility. A popular strategy for portfolio optimization is to use the Mean-Variance model, also known as the Markowitz model. This study aims to form an optimal portfolio consisting of five technology sector stocks in Indonesia with the codes AXIO, DIVA, EDGE, MCAS, and CASH using the Mean-Variance model with assets-liabilities equipped with the ARIMA-GARCH approach. Based on the results of the study, the optimal portfolio is obtained with the composition of each weight is 23.16% of the capital allocated to AXIO; 2.95% for DIVA; 56.48% for EDGE; 6.36% for MCAS; and 11.05% for CASH. The weight allocation composition can generate a portfolio return of 0.0066 and a variance (risk) return of 0.0082.
Modeling Queue Length at The Toll Gate Using Promodel Before and After Ramp-Off Construction Hafizi, Muhamad; Hafiz, Syauqi Abyan; Sugiharto, Bambang; Tosida, Eneng Tita; Bon, Abdul Thalib Bin; Sugara, Victor Ilyas; Subandi, Kotim; Salih, Yasir
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.905

Abstract

In everyday life, queues often occur. Waiting at the counter to get train or movie tickets, at the toll gate, at the bank, at the supermarket, and in other situations that we often encounter Queues occur when the need for services exceeds the capacity or capacity of the service facility. As a result, users of the facility cannot get immediate service due to the busyness of the service. The Amplas Toll Gate queue is the object of this research. The Amplas Toll Gate is one of the densest toll gates that is heavily traveled by vehicles both entering and exiting. This makes it often seen a fairly long queue, especially during peak hours in the late afternoon to evening. The Medan City Government built an off ramp at the Amplas flyover in 2016. This off ramp leads directly to the Amplas toll gate. The vehicle arrival rate increases along with the queue length because vehicles can arrive faster to the toll gate. This study aims to calculate the queue length at the Amplas toll gate before and after the construction of the ramp off. Data is obtained by recording the volume of vehicles at the research location. With an average service time of 7 seconds, the queuing method produces a queue length of 11.98 meters, while the results using Pro Model software are 11.98 meters. In addition, the queue length after the construction of the ramp off decreased to 6.67 meters from before the construction of the ramp off. Promodel is a windows-based simulation software used to simulate and analyze a system.
Strengthening Green Loyalty: How Green Marketing, Green Perceived Value, and Environmental Concern Drive Green Satisfaction (A Study of Uniqlo’s Consumer in Bandung Metropolitan) Septiarini, Eka; Djulius, Horas; Juhana, Dudung
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.883

Abstract

The objective of this study is to identify, examine, and analyze the influence of green marketing on green customer satisfaction, the influence of green perceived value on green customer satisfaction, the influence of environmental concern on green satisfaction, and the influence of green satisfaction on green customer loyalty of Uniqlo consumers in Bandung Metropolitan. Data were collected from respondents aged between 17 and 55 years old, residing in the Bandung Metropolitan area, and having purchased Uniqlo's green products at least twice in the past year. The analysis was performed using Lisrel - Structural Equation Modeling version 8.8. The findings reveal that green marketing, green perceived value, and environmental concern simultaniously contribute 73.6% to green customer satisfaction with Uniqlo in Bandung Metropolitan, while the remaining 26.4% is influenced by other variables. Partially, green marketing contributes 18.5%, green perceived value 24.4%, and environmental concern 30.7% to green satisfaction. Additionally, green satisfaction has been proven to have a significant influence of 79.9% on green loyalty among Uniqlo consumers in Bandung Metropolitans.
Comparison of Stock Price Forecasting with ARIMA and Backpropagation Neural Network (Case Study: Telkom Indonesia) Carissa, Katherine Liora; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.896

Abstract

The growth of capital market investors in Indonesia is increasing every year. The most popular investment instrument is stocks. One of the stocks on the Indonesia Stock Exchange (IDX) is the Telkom Indonesia (TLKM). Through stock investment, investors can make a profit by utilizing stock prices in the market. However, stock price fluctuations are uncertain. Therefore, modeling is needed to be able to predict stock prices more accurately. The purpose of this study was to find an appropriate time series model and Neural Network model architecture, and to measure the accuracy of the two models in predicting future stock prices of TLKM. The study was conducted using the Autoregressive Integrated Moving Average (ARIMA) model and Backpropagation Neural Network (BPNN). For comparison, the Mean Absolute Percentage Error (MAPE) method was used. The data used in both models were the stock prices of Telkom Indonesia (TLKM) from September 1, 2023 to September 30, 2024. The result shows that the best ARIMA model, selected based on the least Akaike Information Criterion (AIC) value, is ARIMA(0,1,3) with a MAPE value of 1.20%. Meanwhile, the best BPNN model selected from the smallest testing Mean Squared Error (MSE) value, is BPNN(1,3,1) with a MAPE value of 1.17%. Among those two models, the BPNN model is more accurate because it has less MAPE value compared to the ARIMA one. The results of this research can be considered in forecasting TLKM stock price in the future.
Mean-Variance Optimal Portfolio Selection with Risk Aversion on Transportation and Logistics Sector Stocks Based on Multi-Criteria Decision-Making Putri, Aulya; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.895

Abstract

The importance of the transportation and logistics sector to a country's economy, coupled with the growth of this sector in Indonesia, requires investment support for this sector to continue to grow. Therefore, stocks in the transportation and logistics sector are attractive for investment portfolio consideration. The optimal portfolio selection is to minimize the risk with the expected return. In the formation of an investment portfolio, the problem is how to determine the weight of capital allocation in order to get the maximum return while still considering the risk in each stock, by considering several criteria in decision making. This study was conducted to determine the best stock selection in the transportation and logistics sector listed on the Indonesia Stock Exchange, and determine the optimal weight in the investment portfolio. The method used is Multi-Criteria Decision Making (MCDM), namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using 15 financial metrics as relevant criteria in stock selection. Furthermore, to determine the allocation weight to form an optimal stock portfolio using the Mean-Variance model with Risk Aversion. The stocks analyzed were 28 stocks in the transportation and logistics sector. The results of research based on MCDM selected 9 stocks, namely MITI, BIRD, HATM, TMAS, JAYA, PPGL, BPTR, ASSA, and RCCC. However, TMAS, PPGL, and BPTR stocks are not included in portfolio formation because they have a negative average return. Based on the optimization results, the allocation weights of the 6 stocks included in the optimal portfolio are BIRD (37.7%), JAYA (24.6%), MITI (12.9%), HATM (9.9%), ASSA (7.5%), and RCCC (7.4%). The results of this study are expected to be a consideration in making investment decisions.
Implementation of the Gated Recurrent Unit (GRU) Model for Bank Mandiri Stock Price Prediction Saputra, Renda Sandi; Pirdaus, Dede Irman; Saputra, Moch Panji Agung
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.894

Abstract

Stock price prediction is a crucial aspect in the financial world, especially in making investment decisions. This study aims to analyze the performance of the Gated Recurrent Unit (GRU) model in predicting Bank Mandiri (BMRI.JK) stock prices using historical data for five years. Stock data was collected from Yahoo Finance and normalized using Min-Max Scaling to improve model stability. Furthermore, the windowing technique was applied to form a dataset that fits the architecture of the time series forecasting-based model. The developed GRU model consists of two GRU layers with 128 neuron units, two dropout layers to prevent overfitting, and one output layer with one neuron to predict stock prices. Model evaluation was carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R² Score) metrics. The experimental results show that the GRU model is able to produce predictions with a high level of accuracy, indicated by the R² Score value of 0.9636, which indicates that the model can explain 96.36% of stock price variability based on historical data.
Implementation of The Apriori Algorithm on X Cafe Sales Transactions for Product Bundling Package Recommendations Sadiah, Halimah Tus; Purnama, Delta Hadi; Erniyati, Erniyati
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.882

Abstract

Bundling packages are a marketing strategy in which several products are combined at a more attractive price than if purchased separately. This strategy effectively increases sales, attracts new customers, and levels up the average transaction value. Cafe X, located in Bogor, is a coffee shop that has not yet had a product bundling strategy package to increase product sales. This study aims to implement the Apriori algorithm on sales transactions at Cafe X to bundle product recommendations. The research stages consist of data collection, preprocessing, implementation of an apriori algorithm, and extracting association rules. In this study, a website-based apriori algorithm was implemented. Users can enter the minimum support value, minimum confidence value, and the recommended menu for product bundling. Based on the research results, it is produced for data input on the application with menu recommendations in the form of Tsuin Iced Coffee and Chicken Strips menus with a minimum support of 50% and a minimum Confidence of 90% can produce recommendations for 3 product bundling packages, including Package 1 recommendations are Tsuin Iced Coffee, Chicken Strips, Hot Barbeque Chicken. Package 2 recommendations are Tsuin Iced Coffee, Chicken Strips, and Nachos. Package 3 recommendations are Tsuin Iced Coffee, Chicken Strips, Hot BBQ chicken and Nachos.
Investment Portfolio Optimization Using Mean-Variance Model With Data Envelopment Analysis (DEA) Approach on IDX30 Stocks Putrie, Veronica Clasrissa
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.865

Abstract

Globalization and technological advancements are driving the importance of careful financial management, including in investments. Stocks have become a popular investment option as they offer potential profits from dividends and capital gains. However, the large selection of stocks in the Indonesian capital market, especially in the IDX30 index, makes investors face challenges in selecting efficient stocks and compiling optimal portfolios. Therefore, this research combines Data Envelopment Analysis (DEA) and Mean-Variance Model to screen efficient stocks and form an optimal investment portfolio. In this study, DEA is used to assess the efficiency of stocks based on company performance, while the Mean-Variance Model is used to determine the optimal weight in the portfolio by balancing risk and return. Of the 13 stocks analyzed, 9 efficient stocks were identified, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, KLBF, TLKM, and UNTR. The optimal portfolio is obtained with a risk tolerance value of 0.015, which results in an expected return of 0.00027711 and a variance of 0.00004396.

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