cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
acengs@umtas.ac.id
Phone
+6285841953112
Journal Mail Official
ijqrm.rescollacomm@gmail.com
Editorial Address
Jalan Riung Ampuh No. 3, Riung Bandung, Kota Bandung 40295, Jawa Barat, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
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 236 Documents
 20   21   22   23   24 
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.
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.
Comparative Analysis of Normal Pension Benefits Using the Attained Age Normal Method and the Individual Level Premium Method Hukama, Atha; Parmikanti, Kankan; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.946

Abstract

Pension programs are among the most important forms of employee compensation, offering financial security after retirement. This study aims to calculate the company’s initial payroll contributions to determine regular contributions, actuarial liabilities, and pension benefits using two actuarial projection methods: the Attained Age Normal (AAN) and Individual Level Premium (ILP) methods. The analysis is based on employee data from Puskesmas Binjai Estate, including age, salary, and years of service. It includes computations of pension benefits, normal costs, actuarial liabilities, and net benefits received by employees under each method. The results reveal that the length of service significantly affects both the value of contributions and the actuarial liabilities. Employees with longer service periods result in higher contribution requirements and greater liabilities. Moreover, the Attained Age Normal method produces higher pension benefits compared to the Individual Level Premium method for long-serving employees. However, both methods present financial challenges for employers, as they require higher contributions relative to the benefits promised. Consequently, companies must allocate substantial funding to meet their pension obligations. This study provides a comparative perspective that can assist decision-makers in selecting an actuarial method that balances benefit adequacy and financial sustainability.
Analysis of the French Five Factors Fama Model on Excess Return of Stocks Listed on IDXBUMN20 for the Period 2020-2023 Putri, Linda Damayanti; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.966

Abstract

Excess return is the difference between the rate of return earned on an investment and the rate of risk-free return in a given period. This shows how much return is received because they are willing to take risks in investing. This study aims to analyze the Fama French Five Factor model on the excess return of stocks listed in IDXBUMN20 2020-2023 period. The factors in the model are market factors, size factors, book to market ratio, profitability, and investment. The population in this study amounted to 20 companies registered in the IDXBUMN20 index, the sample selection in this study used the purposive sampling method and a sample of 12 companies was obtained. The data used in the study are close price, number of shares outstanding, Bank Indonesia (BI) interest rate, and company financial statements. The analysis method used was the Common Effect Model (CEM) panel data regression analysis. Based on hypothesis testing, market factors were obtained which only had an effect on excess returns. This factor shows the influence of the ups and downs of market performance on the price of a stock.
Investment Portfolio Optimization Using Genetic Algorithm on Infrastructure Sector Stocks Based on the Single Index Model Bayyinah, Ayyinah Nur; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.977

Abstract

Investment is a strategic step in managing assets to gain profits in the future by allocating some funds in the present. However, behind the promising potential returns, investment also contains risks that cannot be ignored. One way to reduce the level of risk in investing is to implement a portfolio diversification strategy, which is to form an optimal portfolio by allocating investments to various stocks. This study aims to identify the stocks that form the optimal portfolio, determine the optimal weight of each stock, and calculate the expected return and risk of the portfolio. The portfolio optimization process is carried out using Genetic Algorithm, with the calculation of expected return and risk using the Single Index Model (SIM) approach. The data used includes data on stocks in the infrastructure sector for the period July 1, 2023 to June 30, 2024. The results showed that there were six stocks selected in forming the optimal portfolio with the weight of each stock: PGEO 15.0023%, ISAT 32.1522%, GMFI 4.7822%, EXCL 15.3236%, JSMR 29.7379, and OASA 3.0018%. This optimal portfolio provides an expected return of 0.1167% with a portfolio risk of 0.0152%.
Investment Portfolio Optimization Using Ant Colony Optimization (ACO) Based on Fama-French Three Factor Model on IDX High Dividend 20 Stocks Maharani, Asthie Zaskia; Susanti, Dwi; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.978

Abstract

Stock investment is one of the investment options that provides both profit and risk for investors. In an effort to maximize profits and minimize risks, investors need an optimal portfolio. The optimal portfolio is a portfolio selected from a collection of efficient portfolios. To form an optimal portfolio, this study combines the Fama-French Three Factor Model (FF3FM) for stock selection and Ant Colony Optimization (ACO) for stock weight optimization in the portfolio. FF3FM considers more factors resulting in more comprehensive stock selection than other methods. While ACO has the ability to explore the solution space widely and efficiently, minimizing the risk of getting stuck on a local solution. The performance of the optimal portfolio is measured using the Sharpe Ratio which considers total risk, thus providing an overview of overall investment efficiency. The research object used is quarterly stock data on IDX High Dividend 20 from the Indonesia Stock Exchange (IDX) for the period 2020-2023. Of the 20 stocks, 12 stocks were selected that were consistently included in the index during the 2020-2023 period. By selecting stocks using the FF3FM method, 10 efficient stocks were selected, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, ITMG, PTBA, TLKM, and UNTR. Portfolio optimization using ACO produces a portfolio return of 0.0473 and a risk of 0.0257 with the weight of each ADRO stock of 6.90%, BBCA of 17.24%, BBNI of 10.34%, BBRI of 27.59%, INDF of 3.45%, ITMG of 27.59%, TLKM of 3.45%, and UNTR of 3.45%. The results showed that the integration of FF3FM and ACO was able to form a portfolio with optimal performance with a Sharpe Ratio value of 1.41868, which means that the portfolio return is greater than the portfolio risk.
IDX30 Stock Portfolio Optimization Using Genetic Algorithm Based on Capital Asset Pricing Model Rahmadhisa, Nayra Pavita; Susanti, Dwi; Subartini, Betty
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.981

Abstract

The stock market plays a vital role in supporting economic growth by serving as a primary channel for companies to raise capital and for investors to gain profits through long-term investments. In practice, one of the biggest challenges for investors is identifying which stocks are worth purchasing and how to allocate their funds optimally. One commonly used approach to evaluate stock feasibility is the Capital Asset Pricing Model (CAPM), which helps identify undervalued and overvalued stocks based on the relationship between systematic risk and expected return. Additionally, it is necessary to determine the optimal investment weight allocation. Therefore, this study combines the CAPM method for stock selection and Genetic Algorithm, a metaheuristic approach capable of finding optimal solutions in complex problems, to determine the optimal portfolio weight composition. The object of this study includes stocks listed in the IDX30 index during the period from February 2021 to November 2023. The results show that five stocks—ADRO, BBCA, BBNI, KLBF, and TLKM—are classified as undervalued according to the CAPM method and are recommended for inclusion in the optimal portfolio. Portfolio optimization using the Genetic Algorithm results in the following stock weight composition: ADRO 26.55%, BBCA 36.20%, BBNI 9.09%, KLBF 12.20%, and TLKM 15.96%, with a Sharpe Ratio of 4.043906. The expected return and risk of the optimal portfolio are 0.00067373 and 0.00012407, respectively.
Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sianturi, Sri Novi Elizabeth; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1001

Abstract

Stock mutual funds gained popularity among the public as an investment alternative due to the convenience they offer, especially for beginner investors who have limited time and investment knowledge. Compared to money market and bond mutual funds, these mutual funds offer higher potential returns but also come with higher risks due to value fluctuations, so forecasting stock mutual fund prices is essential to minimize losses. Since stock mutual fund prices is time series data, this research employs two forecasting models such as Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR). The objective of this research is to determine the best-performing model between ARIMA and NNAR, and compare their forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The data used consists of daily closing prices of stock mutual funds from March 1, 2022, to March 31, 2025, with the criteria that the selected issuers have been operating for more than five years. The results of this research show that the best ARIMA and NNAR for the RNCN are ARIMA([1],1,0) and NNAR(2,2); for TRAM are ARIMA(0,1,[1]) and NNAR(4,1); for SCHRP are ARIMA(0,1,[1]) and NNAR(4,2); for MICB are ARIMA([1],1,0) and NNAR(2,2); and for BNPP are ARIMA([1],1,0) and NNAR(5,1). The MAPE values in the same order are 6.83% and 5.49%; 6.53% and 5.75%; 8.57% and 7.10%; 8.39% and 8.75%; 8.51% and 7.30%. Based on the comparison, NNAR outperformed ARIMA in four out of five mutual funds, with lower MAPE values and also marked by the ARIMA model tend to produce stable or unchanging values over the long term. The results of this research are expected to assist investors in consederating by choosing NNAR model, both in the short and long term, to obtain better stock mutual fund price forecasts.
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Febrianty, Popy; Napitupulu, Herlina; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1002

Abstract

Stock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311.
Portofolio Optimization of Mean-Variance Model Using Tabu Search Algorithm with Cardinality Constraints Ma’mur, Lutfi Praditia; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1010

Abstract

Stock investment is increasingly attractive to Indonesians, especially through the IDX30 index, which is known to have high liquidity and solid company fundamentals. In forming an optimal stock portfolio, investors are faced with the challenge of maximizing return and minimizing risk simultaneously. An optimal portfolio is defined as a combination of assets that provides the highest expected return at a certain level of risk, or the lowest risk for the expected level of return. This study aims to form an optimal portfolio on the IDX30 index by considering cardinality constraints, which limit the maximum number of stocks in the portfolio. From 30 IDX30 stocks, 20 stocks were selected based on consistency of existence during the period February 1, 2023 to January 31, 2025. Next, 8 stocks that have positive expected return values are selected, and from these 8, 4 efficient stocks are selected using cardinality constraints. Selection is done with the Tabu Search algorithm, a memory-based metaheuristic optimization method used to find the best solution by avoiding previously explored solutions. The portfolio is formed using the Mean-Variance model, resulting in an allocation of BMRI (30,02%), PTBA (35,18%), INDF (2,48%), and BRPT (32,32%), with an expected return of 0,00207 and a variance of 0,001587.

Page 23 of 24 | Total Record : 236

 20   21   22   23   24 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM) Vol 6, No 2 (2025) Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol 6, No 1 (2025) Vol 5, No 4 (2024) Vol. 5 No. 4 (2024) Vol. 5 No. 3 (2024) Vol 5, No 3 (2024) Vol. 5 No. 2 (2024) Vol. 5 No. 1 (2024) Vol 4, No 3 (2023) Vol 4, No 2 (2023) Vol 4, No 1 (2023) Vol 3, No 4 (2022) Vol 3, No 3 (2022) Vol 3, No 2 (2022) Vol 3, No 1 (2022) Vol 2, No 4 (2021) Vol 2, No 3 (2021) Vol 2, No 2 (2021) Vol 2, No 1 (2021) Vol 1, No 4 (2020) Vol 1, No 3 (2020) Vol 1, No 2 (2020) Vol 1, No 1 (2020) More Issue