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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 236 Documents
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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.
Comparison of Random Forest and SVM Algorithms in Classification of Diabetic Retinopathy Based on Fundus Image Texture Features Saputra, Renda Sandi; Saputra, Moch Panji Agung
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.v6i2.1011

Abstract

Diabetic Retinopathy (DR) is a microangiopathic complication of diabetes mellitus that can cause visual impairment to permanent blindness. Early detection of DR is essential to prevent disease progression, but conventional methods require time, cost, and expertise that are not always available. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in DR classification based on texture features extracted from retinal fundus images. The dataset used consists of 3,000 retinal fundus images obtained from the Kaggle platform, divided into 2,400 training data and 600 test data. Image preprocessing includes conversion to grayscale, resizing to a resolution of 128×128 pixels, and normalization. Feature extraction is performed using a combination of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) to produce a 14-dimensional feature vector. Performance evaluation uses accuracy, precision, recall, F1-score, ROC curve, and 5-fold cross-validation metrics. The results showed that Random Forest significantly outperformed SVM with an accuracy of 96% compared to 64%, an AUC value of 0.99 compared to 0.72, and an average cross-validation accuracy of 94.5% compared to 63.42%. Random Forest also showed balanced performance in both classes with precision, recall, and F1-score of 0.96, while SVM experienced classification imbalance especially in the disease class. This study proves that Random Forest is a more optimal algorithm for an automatic DR detection system based on fundus image texture features and can support increasing the accessibility of DR screening in areas with limited specialist medical personnel.
Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Nurdyah, Himda Anataya; 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.v6i2.1017

Abstract

The importance of the food sector to Indonesia's economy makes it one of the most attractive sectors to consider in an investment portfolio. An optimal portfolio is the best choice for investors among various efficient portfolios, aiming to maximize returns while minimizing risk. Moreover, since investment is inherently associated with fluctuating stock prices, accurate forecasting is necessary to anticipate future stock movements. This study aims to accurately predict stock prices and construct an optimal portfolio consisting of five food sector stocks listed on the Indonesia Stock Exchange, namely DMND, ICBP, HOKI, INDF, and ULTJ. Stock price predictions are generated using the Holt-Winter method, which can identify seasonal patterns and trends from historical data. The predicted stock prices are then used to calculate returns, which serve as the basis for portfolio optimization using the Mean-Variance model. The results show that the Holt-Winter method successfully produces accurate stock price forecasts, with Mean Absolute Percentage Error (MAPE) values for all stocks below 10%. These forecasts are used to calculate returns in the portfolio optimization process. The optimal portfolio composition is determined with the following weight proportions: HOKI (4%), ICBP (18%), ULTJ (21%), DMND (26%), and INDF (30%). This portfolio yields an expected return of 0.0441% and a portfolio variance of 0.0063%, reflecting a balanced trade-off between potential return and risk.
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.
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.

Page 14 of 24 | Total Record : 236

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