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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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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%.
Implementation of the Gated Recurrent Unit (GRU) Model for Bank Mandiri Stock Price Prediction Saputra, Moch Panji Agung; Saputra, Renda Sandi; Pirdaus, Dede Irman
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.
Comparison of Random Forest and SVM Algorithms in Classification of Diabetic Retinopathy Based on Fundus Image Texture Features Saputra, Moch Panji Agung; Saputra, Renda Sandi
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.
A Comparative Study of Projected Unit Credit and Attained Age Normal Methods for Actuarial Liability Estimation: A Case Study of PT Taspen Muqtashida, Amalia Aura; Melania, Suryaningrum Virgia
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1027

Abstract

The selection of an appropriate actuarial liability calculation method will help PT Taspen (Persero) to ensure the adequacy of pension funds that must be prepared, optimize pension fund management, and reduce the risk of future underfunding. This study aims to analyze the comparison between the Projected Unit Credit and Attained Age Normal methods in the context of estimating actuarial liabilities at PT Taspen (Persero), and evaluate how the differences in these methods affect pension fund planning and management. Through this analysis, it is expected to find a method that is more suitable for the characteristics of pension participants and the long-term needs of PT Taspen (Persero) in ensuring the sustainability of an efficient pension program. Comparative descriptive research was conducted in this study to describe the comparison of the results of estimating actuarial liabilities from two methods: Projected Unit Value (PUC) and Attained Age Normal (AAN) and assess their impact on funding conditions. In total, the value of actuarial liabilities generated by the PUC method is slightly higher at Rp421,875,241,393.40, compared to the AAN method of Rp420,746,185,877.40. This difference shows that the method used in the calculation greatly affects the amount of liabilities that must be met by the pension fund.
The Influence of Capital Structure on Profitability: Panel Regression Analysis of Indonesian State-Owned Enterprises in the Energy and Mining Sector from 2019 to 2023 Putri, Najmah Rizqya Maliha; Fernanda, Adeliya
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1028

Abstract

Capital structure is an important factor in financial decision-making that can influence a company's profitability level. Indonesian state-owned enterprises (BUMN) in the energy and mining sector have high capital needs and significant exposure to external risks, making capital structure efficiency crucial. This study aims to analyze the impact of Debt to Asset Ratio (DAR) and Debt to Equity Ratio (DER) on Return on Equity (ROE) as a profitability indicator for Indonesian state-owned enterprises in the energy and mining sector in Indonesia during the period 2019–2023. This research uses six companies as samples, namely PT Aneka Tambang Tbk., PT Bukit Asam Tbk., PT Indonesia Asahan Aluminium, PT Pertamina (Persero), and PT Timah Tbk. The study employs a quantitative approach with a panel data regression method. Data was obtained from the annual financial statements of the company. The analysis process was conducted thoroughly using Eviews 12 software, including data processing, assumption testing, selection of the panel regression model, and final estimation. The results of the analysis indicate that the Random Effect Model is the most suitable approach. Simultaneously, DER and DAR have a significant effect on ROE. However, partially, only DER has a significant negative effect, while DAR is not significant. These findings indicate that the capital structure, specifically the proportion of debt to equity, plays an important role in determining the company's profitability. Therefore, optimal management of the financing structure becomes an important strategy for the company in maintaining long-term financial performance.
The Impact of Salary Increase Projection Assumptions on the Difference in Pension Liability Value between the Projected Unit Credit and Traditional Unit Credit Methods Aziz, Sena Miftahul; Perdana, Oksigeno Phanca
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1029

Abstract

The calculation of pension liabilities in defined benefit plans is highly influenced by the actuarial method and economic assumptions applied, particularly salary growth projections. This study aims to analyze the impact of salary increase assumptions on pension liabilities using two commonly adopted actuarial methods: Projected Unit Credit (PUC) and Traditional Unit Credit (TUC). Simulations were conducted using dummy data across three salary groups with varying annual salary growth assumptions, allowing for a comparative analysis of the resulting liabilities from both methods. The results show that PUC consistently produces significantly higher pension liabilities than TUC, with the difference increasing as the assumed salary growth rate rises. This demonstrates the higher sensitivity of the PUC method to future salary projections, which may lead to a more realistic but financially heavier burden on the company. This study offers valuable insights for decision makers in selecting appropriate actuarial methods for pension liability valuation based on their financial strategies and risk tolerance.
Comparison of Dengue Hemorrhagic Fever (DHF) Cases Between Male and Female in Bandung City Maharani, Nisrina Deyan; Wijaya, Indri; Germana, Gilang; Zhafira, Aqilah Febrianti; Hafiza, Ayesha Ghania; Tiara, Lovely; M, Ali Fatih; Azkia, Safa Nur; Fathia, Nur Rusyda; Sehan, Putri Nabilah; Subekti, Rahma Oka; Hadi, Rahmat Hibatul; Hidayat, Yuyun
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1038

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease that is a serious health problem in tropical regions such as Indonesia, including Bandung City, which has a high population density and environmental conditions that support the development of the Aedes aegypti vector. This study aims to determine the difference in the number of DHF cases between men and women in Bandung City during the period 2023-2024. Secondary data were obtained from two different sources and analyzed using the chi-squared statistical test. The combined results show that the number of DHF cases in men was 1,607 cases, which shows a higher number compared to women, who had 1,590 cases. The calculated value in 2023 was 0.65, and in 2024, it was 0.015. While the combined data in 2023-2024 had a X2 Value of 0.5708. This shows that the results of the three calculations are smaller than the X2 Table value of 3.84 (α = 0.05; df = 1), so the null hypothesis is accepted and the alternative hypothesis is rejected. Thus, it can be concluded that there is no significant difference in the number of dengue fever cases between men and women. This finding suggests that gender is not a determining factor in the spread of dengue fever cases in Bandung City.
The Dynamic Impact of Foreign Debt-Based Education and Health Investment on Economic Growth in Asean-5 Countries Qusrinda, Ade; Syahnur, Sofyan; Nasir, Muhammad
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1041

Abstract

This study examines the use of external debt to finance health and education in order to promote economic growth in developing countries, focusing on five ASEAN member countries namely Cambodia, Indonesia, Laos, the Philippines and Thailand. The Johansen, Pedroni and Kao cointegration test results indicate the existence of a long-run relationship between the independent and dependent variables. The panel data Autoregressive Distributed Lag (ARDL) model is used to analyze the short-term and long-term effects using annual data for the period 2000-2022. The results of this study found that in the short run education financing has a positive effect while health, labor and capital financing have a negative impact on economic growth. The results in the long run found that education and health financing have a negative impact on economic growth in ASEAN-5 countries due to too high debt and inefficiency in allocation is also one of the reasons the long-term effect has not been realized. Labor and capital have a positive impact on economic growth this is due to high external debt in many ASEAN-5 countries is also high, although this is not proportional to external debt and the effect is very small. Based on the findings of this study, it is recommended that governments in ASEAN-5 countries continue to improve efficiency in managing and allocating foreign debt towards education and health. In addition, serious efforts are needed for more assertive and targeted policies related to the use of foreign debt.
The Effect of Macroeconomic Variables on Indonesia's Import Value Using the OLS Method Januaviani, Trisha Magdalena Adelheid; Kalfin; Hutabarat, Aned Miranda; Nikita; Musdaifah, Selvy; Nacong, Nasria
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1070

Abstract

This study analyzes the factors influencing Indonesia’s import value during the period 2021–2025 using the Ordinary Least Squares (OLS) method. To ensure the validity of the model, a series of classical assumption tests were conducted in accordance with the Best Linear Unbiased Estimator (BLUE) criteria, including tests for normality, multicollinearity, heteroscedasticity, and autocorrelation. The data were obtained from official publications of the Central Statistics Agency (BPS) and other relevant sources. The estimation results demonstrate that the independent variables, namely the exchange rate (X₁), national income (X₂), foreign exchange reserves (X₃), inflation rate (X₄), and interest rate (X₅), exert varying effects on Indonesia’s import value, with certain variables exhibiting significant influence while others remain insignificant. The model is free from violations of the classical assumptions, thereby meeting the criteria of the Best Linear Unbiased Estimator (BLUE). Keywords: Import Value, OLS, Classical Assumption Tests, Macroeconomics
Cryptographic Security for Double Encryption on Images Using AES and IDEA Algorithms Nizar Septi maulana; Asep Id Hadiana; Melina
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1072

Abstract

Di era digital, keamanan citra rekam medis elektronik telah menjadi perhatian utama karena tingginya risiko kebocoran informasi sensitif. Studi ini menyelidiki dan mengembangkan implementasi enkripsi ganda pada data citra medis dengan menggabungkan Advanced Encryption Standard (AES) dan International Data Encryption Algorithm (IDEA) untuk meningkatkan keamanan citra, khususnya untuk citra rekam medis elektronik yang rentan terhadap pelanggaran informasi. AES digunakan karena efisiensinya dalam mengenkripsi data berukuran besar, sementara IDEA menawarkan struktur kunci yang kompleks yang memberikan perlindungan yang lebih kuat terhadap akses yang tidak sah. Kumpulan data citra Magnetic Resonance Imaging (MRI) yang diperoleh dari platform publik Kaggle digunakan sebagai objek uji untuk proses enkripsi dan dekripsi. Evaluasi dilakukan dengan menggunakan dua pendekatan utama: uji efek avalanche untuk mengukur sensitivitas perubahan input terhadap output ciphertext, dan uji waktu pemrosesan untuk menilai efisiensi kinerja enkripsi dan dekripsi. Hasilnya menunjukkan nilai rata-rata efek avalanche mencapai 49,97%, sangat mendekati nilai ideal 50%, menunjukkan tingkat difusi data yang tinggi dan kekuatan kriptografi yang kuat. Sementara itu, pengujian waktu enkripsi pada lima berkas citra menunjukkan bahwa rata-rata waktu yang dibutuhkan untuk melakukan enkripsi ganda menggunakan AES dan IDEA adalah 52,06 detik, dengan rentang waktu antara 42,0 detik hingga 63,5 detik, tergantung pada ukuran dan kompleksitas citra. Oleh karena itu, kombinasi AES dan IDEA terbukti meningkatkan kekuatan kriptografi tanpa mengurangi efisiensi operasional secara signifikan. Pendekatan enkripsi ganda ini dinilai layak dan efektif untuk diimplementasikan dalam sistem informasi kesehatan, terutama untuk menjaga kerahasiaan, integritas, dan keaslian citra rekam medis elektronik. Kata kunci: Enkripsi ganda, AES, IDEA, efek longsor, keamanan citra medis, efisiensi waktu pemrosesan

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