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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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Digital Image Security with AES and Blowfish Double Encryption Iqbal Dwi Nulhakim; 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.v6i3.1073

Abstract

Protection of digital images is becoming increasingly important with the growing use of images as a medium of information in various fields, particularly in the healthcare sector. Medical images such as Magnetic Resonance Imaging (MRI) contain sensitive information that requires extra security against unauthorized access and data manipulation. This study aims to design and build a digital image security system using a dual encryption approach and authenticity verification based on watermarking. The security process is carried out in two main stages. First, images with text-based watermarks are encrypted using the Advanced Encryption Standard (AES) algorithm to protect their visual content. Second, the AES key is re-encrypted using the Blowfish algorithm to prevent the key from being stored in plaintext, thereby creating an additional layer of protection. The watermark is embedded into the image using the Singular Value Decomposition (SVD) method and is first converted into a hash value using the SHA-256 algorithm, which serves to verify the integrity of the image after decryption. The testing was conducted using the public dataset “Brain Tumor Image Dataset (Semantic Segmentation)” from Kaggle, which consists of brain MRI images in .jpg and .png formats. The system evaluation encompassed functionality, data security, and process efficiency through system function testing, measurement of encrypted data randomness (entropy test), file penetration using OpenSSL, and performance analysis in terms of processing time and file size. The research results show that the system successfully implemented double encryption with a high entropy level (approaching 8.00) and resistance to penetration attacks. In terms of efficiency, the system achieved an average encryption time of 81.35 ms and decryption time of 13.68 ms with minimal file size increase. Integrity testing confirmed that the SVD-SHA256-based watermark remained intact after the encryption-decryption process, enabling verification of image authenticity. The developed system efficiently maintains the confidentiality and authenticity of digital images and can be applied in electronic medical record systems or sensitive digital archives. Keywords: Digital images, double encryption, AES, Blowfish, SVD
Premium Sufficiency Reserve on Joint Life Insurance with Laplace Distribution Triyuni, Meisy; Sirait, Haposan
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.v6i3.1074

Abstract

Each insurance participant pays a premium to the insurance company during the coverage period. In paying the sum insured to insurance participants, insurance companies need to prepare reserve costs. This reserve fee is used to pay for the needs of insurance companies and insurance participants. This research explains the calculation of premium sufficiency reserve for joint life insurance for life insurance participants aged x years and y years with Laplace distribution. The parameters of the Laplace distribution are estimated using the method of momen and the method of maximum likelihood. The solution of the problem is obtained by determining the initial life annuity term, single premium, and annual premium so that the premium sufficiency reserve formula based on Laplace distribution is obtained. The results of the calculation of premium sufficiency reserves of joint life insurance using Laplace distribution are more less the same as the prospective reserves of joint life insurance using Laplace distribution.
Implementation of Dynamic Programming Algorithm on The Integer Knapsack Problem (0/1) (Case Study: J&T Cargo Agent Purwokerto) Puspitasari, Leni; Sugandha, Agus; Nurshiami, Siti Rahmah
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.v6i3.1077

Abstract

The pu IDR ose of this research is to solve the 0/1 integer knapsack problem, which is a problem of selecting items from a number of available items where each item has different weights and profits. The delivery of items at J&T Cargo Purwokerto is one of many item selection problems. The delivery of items at J&T Cargo Purwokerto is carried out progressively with higher profit values ​​first, due to the delivery capacity being able to accommodate only 700 kg. In order for J&T Cargo Purwokerto to obtain maximum profit, item selection for delivery must be carried out first. The item selection at J&T Cargo Purwokerto can be solved using the 0/1 integer knapsack problem method with a forward recursive dynamic programming algorithm with the help of Matlab R2021A software. The results of the research indicate that on July 1, 2025, a maximum profit of IDR 3,038,850 was achieved with a weight of 700 kg. On 2nd July 2025, a maximum profit of IDR 4,884,985 was achieved with a weight of 700 kg. On 3rd July 2025, a maximum profit of IDR 7,732,155 was achieved with a weight of 699 kg.
Multiple Linear Regression Analysis of Factors Influencing Human Development Index By Regency/City in East Java Province in 2024 Itsnaini, Nurul; Prabowo, Agung; Amitarwati, Diah Paramita
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.v6i3.1078

Abstract

The Human Development Index (HDI) is an indicator used to assess the success of human development. The Human Development Index (HDI) is used to measure the impact of efforts to improve basic human capital capabilities. Based on BPS data, the HDI in East Java has consistently increased and has reached a high category, however, when compared to DKI Jakarta and DI Yogyakarta, the HDI in East Java is still relatively low. This study aims to determine the factors that influence the HDI in East Java Province. The research data are the 2024 HDI data for East Java Province obtained from the BPS of Lamongan Regency and the BPS website of East Java Province. This study uses the multiple linear regression method with RStudio software. Based on the results of the study, the multiple linear regression model with HLS, RLS, UHH, and GK factors has an influence of 97.94% on the HDI in East Java Province, while the TPT does not show a significant influence on the HDI in East Java Province.
Comparison of Islamic and Conventional Bank Stock Portfolio Performance Using the Markowitz Model: Risk and Return Analysis on Four Selected Issuers Laila, Aliffatul; Janitha, Asrie Putri
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

Abstract

This study aims to compare the performance of Islamic and conventional bank stock portfolios in Indonesia using the Markowitz Model approach that focuses on return and risk optimization. The object of research includes four banking issuers, namely BBCA and BBNI (conventional), and BRIS and BTPS (sharia), with daily closing price data during the period March 2020 to March 2021. Calculations were made on expected return, risk (standard deviation), sharpe ratio, and optimal portfolio composition. The results show that the Islamic stock portfolio has a higher expected return (0.009385) than the conventional portfolio (0.001652), but is accompanied by greater risk. Nevertheless, the efficiency of the Islamic portfolio remains competitive based on the sharpe ratio indicator and the ratio of return to variance. The optimal composition in the Islamic portfolio is dominated by BTPS stocks (68.69%), while in the conventional portfolio it is dominated by BBNI stocks (62.19%). These findings suggest that an Islamic bank stock portfolio can be an investment alternative that is not only ethical, but also financially superior in terms of risk and return.
Actuarial Analysis of PNS Group III/D Pension Fund: Comparison of Projected Unit Credit and Individual Level Premium Methods Fernanda, Adeliya; Putri, Najmah Rizqya Maliha
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.v6i3.1026

Abstract

Indonesia’s Civil Servants (PNS) pension system uses a defined benefit scheme managed by PT Taspen (Persero). However, the scheme faces serious challenges such as increasing life expectancy, a growing number of retirees, and an imbalance in pension contributions and liabilities. Evaluation of the liability calculation method is important to ensure the sustainability of the system. This study aims to compare the Projected Unit Credit (PUC) and Individual Level Premium (ILP) methods in calculating the pension fund for PNS Group III/D. This research uses a quantitative approach through actuarial simulation of data on civil servants of Group III/D with the assumptions of salary, retirement age, and annual salary increase. The analysis is done by calculating Actuarial Liability and Normal Cost for each method. The results show that the PUC method produces a Normal Cost that increases with the age of participants, while ILP provides a fixed contribution even though it is larger at the beginning. Both Actuarial Liability values also increase as the retirement age approaches, but ILP tends to be higher at all ages. From the manager's perspective, ILP is more stable and planned, while PUC is lighter on participants at the beginning and takes into account salary increases. Therefore, the choice of method must consider the ability of the agency to pay contributions consistently and the expectations of participants to get decent retirement benefits. The results of this study are expected to be taken into consideration in improving a fairer and more sustainable pension system for PNS, especially Group III/D.
Design of A Decision Support System for Students' Extracurricular Choices using the TOPSIS Method at SMKN 1 Bukit Sundi Afdal; Putra, Yendi; Yulhan; Putri, Etika Melsyah
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.v6i3.1093

Abstract

Education not only emphasizes academic excellence but also requires the development of students' character, talents, and soft skills. Extracurricular activities play a crucial role in providing students with opportunities to explore their potential beyond the classroom. However, students often encounter difficulties in selecting the most suitable extracurricular activity, which may result in low motivation and reduced participation. To address this issue, a web-based Decision Support System (DSS) was developed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The system considers multiple criteria, namely interest, talent, academic achievement, parental support, physical condition, and equipment cost, to generate objective recommendations. The research was conducted at SMKN 1 Bukit Sundi with seven extracurricular alternatives, including Futsal, Volleyball, Pramuka, Paskibra, Marching Band, OSIS, PMR, and Randai. The system was implemented using PHP and MySQL, providing an automated process of normalization, weighting, calculation of ideal solutions, and ranking. Results showed that Futsal achieved the highest preference value (0.755846), followed by Volleyball and Pramuka, while Randai ranked lowest with a value of 0.223794. These findings indicate that student preferences are strongly aligned with sports and leadership activities, while traditional art forms are less favoured. The system proved to be consistent with manual calculations and successfully enhanced transparency, efficiency, and accessibility in extracurricular selection. Compared to alternative methods such as SAW, TOPSIS offered greater flexibility by accommodating both benefit and cost attributes simultaneously. This study contributes practically by providing a tool that supports schools and students in decision-making, and academically by extending the application of TOPSIS in vocational education.
Comparative Analysis of the Effectiveness Between Malwarebytes and BitDefender to Prevent Malware Attacks Yohanza, Mohammad Zidan; Giat, Muhammad; Fadhilah, Muhammad Iksan; Sulaeman, Mohammad; Iskandar, Ibrahim Dafi; Hidayat, Yuyun
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.313

Abstract

Malware is the largest type of cyber-attack case in Indonesia. With the number of cases of malware occurring, many emerging software that provides services to ward off malware attacks. It takes the most effective anti-malware software to ward off malware attacks, so research is carried out. This study tested the detection and removal power of two anti-malware software (BitDefender and Malwarebytes). The initial research method used is to make a Pilot test which is a prefix in malware testing. In the Pilot test, the initial testing process for anti-malware software is carried out. Software that tested in the Pilot test include Malwarebytes, BitDefender, Avast, Cybereason, AVG, Avira. In the Pilot test, as many as 30 malwares were tested to determine which two software had the highest percentage of detection and removal tests. Furthermore, the data from the previous test got analyzed using the proportion of two populations test to determine the most effective software. With the tests of 500 malwares, it was found that the proportion of detection and removal of the BitDefender software is better than the Malwarebytes software. Therefore, it can be concluded that the BitDefender software is more effective than the Malwarebytes software as seen from the results of the test of the proportion of malware detection and removal.
Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application Sadiah, Halimah Tus; Purnama, Delta Hadi; Ishlah, Muhamad Saad Nurul
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.552

Abstract

This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form
Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques Yadav, Nikesh
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.567

Abstract

Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices.

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