International Journal of Quantitative Research and Modeling
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
Articles
236 Documents
<![CDATA[Waiting Time Optimization at Traffic Light Intersection in Purbalingga by Using Compatible Graphs ]]>
<![CDATA[Lestari, Mugi]]>;
<![CDATA[Maryani, Sri]]>;
<![CDATA[Halim, Nurfadhlina Abdul]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.742
<![CDATA[Traffic congestion is a problem that often occurs at crossroads. One of the causes of congestion is the waiting time for traffic at a crossroad improper, so it can cause the accumulation of vehicles in several branches. The purpose of this paper is to determine the optimal waiting time for traffic lights at the Sudirman-Pujowiyoto intersection in Purbalingga by using a compatible graph. The traffic flow at the intersection can be modeled into a compatible graph, where a vertex represents the traffic flow to be managed and the edges indicate that the two flows are compatible. It means that they can run simultaneously without crossing. Based on secondary data from Dinas Perhubungan Kabupaten Purbalingga, the total waiting time applied to the Sudirman-Pujowiyoto intersection is 317 seconds. Meanwhile, according to the compatible graph calculation, by using the assumption of 60 seconds in a cycle, an optimal total waiting time is 120 seconds.]]>
<![CDATA[The Application of Z-Score and Zavgren Models in Managing Financial Distress at PT Garuda Indonesia (Persero) Tbk ]]>
<![CDATA[Damayanti, Resma]]>;
<![CDATA[Putri, Aulya]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.826
<![CDATA[As an archipelago, the aviation sector in Indonesia plays an important role, but PT Garuda Indonesia (Persero) Tbk. as one of the airline companies has experienced significant financial pressure. In the third quarter of 2023, the company recorded a net loss of US$ 72.07 million. This condition may put the company at risk of financial distress, a situation in which the company experiences financial difficulties before bankruptcy. This study uses the Altman Z-Score Model and the Zavgren Model to predict potential financial distress at PT Garuda Indonesia (Persero) Tbk. The analysis results show that from 2021 to 2023, the Altman Z-Score is consistently in the Bankrupt category, reflecting a high risk of bankruptcy, while the Zavgren model shows vulnerable conditions in 2021 but also indicates bankruptcy in 2022 and 2023. The results of this study are expected to provide early warning and assist management decision-making to reduce the risk of bankruptcy.]]>
<![CDATA[Securing Network Log Data Using Advance Encryption Standard Algorithm And Twofish With Common Event Format ]]>
<![CDATA[Ali, Moch. Dzikri Azhari]]>;
<![CDATA[Hadiana, Asep Id]]>;
<![CDATA[Melina, Melina]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.757
<![CDATA[The rapid advancement of information technology demands enhanced security for data exchange in the digital world. Network security threats can arise from various sources, necessitating techniques to protect information transmitted between interconnected networks. Securing network logs is a critical step in strengthening overall network security. Network logs are records of activities within a computer network, including unauthorized access attempts, user activities, and other key events. This research focuses on developing a network log security system by comparing the performance of the Advanced Encryption Standard (AES) and Twofish algorithms, integrated with the Common Event Format (CEF) for encrypting network logs. Tests were conducted on network log datasets to evaluate system functionality and performance. Results indicate that the AES algorithm performs encryption and decryption faster than Twofish. Across five tests with different file sizes, AES took an average of 2.1386 seconds for encryption, while Twofish required 22.8372 seconds. For decryption, AES averaged 2.451 seconds compared to Twofish’s 26.140 seconds. The file sizes after encryption were similar for both algorithms. Regarding CPU usage, AES demonstrated higher efficiency. The average CPU usage during AES encryption was 0.5558%, whereas Twofish used 23.2904%. For decryption, AES consumed 0.4682% of CPU resources, while Twofish required 13.7598%. These findings confirm that AES is not only faster in both encryption and decryption but also more efficient in terms of CPU usage. This research provides valuable insights for optimizing network log security by integrating standardized log formats, like CEF, with appropriate encryption techniques, helping to safeguard against cyber threats.]]>
<![CDATA[Premium Sufficiency Reserve of Last Survivor Endowment Life Insurance using Exponentiated Gumbel Distribution ]]>
<![CDATA[Putri, Viona Sephia]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.746
<![CDATA[Life insurance is a protection effort provided by the insurer against risks to the insured’s life that will arise from an unpredictable event. Insurance companies are required to prepare reserves to fulfill the sum insured when a claim occurs. Premium sufficiency reserves are modified reserves whose calculations use gross premiums that contain administrative maintenance costs. The purpose of this study is to determine the amount of premium sufficiency reserves of endowment life insurance for two insurance participants aged x years and y years using the exponentiated Gumbel distribution. The parameters of the exponentiated Gumbel distribution are estimated using the maximum likelihood method and then determined by a Newton-Raphson iteration method. The solution of the problem is obtained by determining the initial life annuity term, single premium, and annual premium so as to obtain the reserve formula of the premium sufficiency of the last survivor status endowment life insurance using the exponentiated Gumbel distribution. The results of the calculation of reserves premium sufficiency of endowment life insurance last survivor status using the exponentiated Gumbel distribution is slightly smaller than premium sufficiency reserve for endowment life insurance using the Indonesian Mortality Table 2019.]]>
<![CDATA[Comparative Analysis: Value at Risk (VaR) with Parametric Method, Monte Carlo Simulation, and Historical Simulation of Mining Companies in Indonesia ]]>
<![CDATA[Darmawan, Muhammad Rizky]]>;
<![CDATA[Widyono, Fathi Atha Putra]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.834
<![CDATA[This study aims to conduct a comparative analysis between three Value at Risk (VaR) calculation methods, namely the Parametric (Variance-Covariance) method, Monte Carlo Simulation, and Historical Simulation, in measuring market risk in mining companies in Indonesia. The mining industry in Indonesia faces the risk of high commodity price volatility, thus requiring an appropriate approach in measuring potential financial losses. This study uses historical stock data from several major mining companies in Indonesia to analyse the difference in results between the three VaR methods. This study found that the smallest VaR value is owned by PTBA company. Along with the level of stability shows that PTBA company is more stable than other companies. This is inversely proportional to the TINS company which has a large VaR value and high volatility.]]>
<![CDATA[Risk Prediction and Estimation of Corporate Product Claim Reserve Funds in Insurance Companies Using the Extreme Value Theory ]]>
<![CDATA[Maelowati, Indah Dewi]]>;
<![CDATA[Mayaningtyas, Chibi Adinda]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.820
<![CDATA[Every human action involves risk, and in the insurance industry, customer claims are the biggest risk that companies face. This risk must be managed effectively through claim prediction, especially for corporate products. This research analyzes the risk of claims at insurance companies using the Extreme Value Theory (EVT) method, which can estimate extreme risks. Identification of extreme values in claims data is done through the EVT approach, namely Block-Maxima (BM). Generalized Extreme Value (GEV) distribution parameter estimation is performed, followed by prediction of claim risk using Value at Risk (VaR) and estimation of claim reserve funds. The results show that the GEV approach with a 95% confidence level is most suitable for predicting claim risk. Based on these results, the company requires a claim reserve fund of IDR 100,798,248,000 to deal with potential losses due to extreme claims.]]>
<![CDATA[Enhancing Email Client Security with HMAC and PGP Integration to Mitigate Cyberattack Risks ]]>
<![CDATA[Oktaviani, Ayu Nur]]>;
<![CDATA[Hadiana, Asep Id]]>;
<![CDATA[Melina, Melina]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.758
<![CDATA[The rapid advancement of technology in the modern era has significantly increased the risk of data breaches and misuse, particularly in email communications. Ensuring data privacy and security is crucial to preventing information theft and mitigating cyberattack risks. This research focuses on enhancing email client security through the integration of Hash-Based Message Authentication Code (HMAC) and Pretty Good Privacy (PGP). HMAC is employed as a message authentication mechanism to ensure the integrity and authenticity of email messages, while PGP is utilized to generate public and private key pairs, enabling secure encryption and decryption processes. By integrating these two security methods into the email client system, we aim to enhance its resilience against cyber threats. The system's effectiveness was evaluated through black-box testing, demonstrating its capability to secure the email delivery process. Additionally, an analysis of key randomness using the entropy method revealed a maximum value of 6 bits, indicating a relatively high level of randomness and further strengthening the encryption process. The results of this study indicate that the combined use of HMAC and PGP provides a robust security solution for enhancing email client security and mitigating potential cyberattack risks.]]>
<![CDATA[Mean-Variance Portfolio Optimisation Model for Comparison of Stock Portfolio Composition on the American Stock Exchange before and after the Boycott of Companies Supporting Israel (Case Study: AAPL, SBUX, AMZN, GOOGL, MCD) ]]>
<![CDATA[Fasa, Rayyan Al Muddatstsir]]>;
<![CDATA[Sukono, Sukono]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i3.744
<![CDATA[The background of this research is related to the boycott of companies that support Israel, which affects the composition of stock portfolios on the American Stock Exchange. The focus of this research is on key companies such as Apple (AAPL), Starbucks (SBUX), Amazon (AMZN), Google (GOOGL), and McDonald's (MCD). The problem to be solved is the identification of changes in optimal asset allocation in investment portfolios before and after the boycott. Using a mean-variance portfolio optimization model, historical stock price data is analyzed to model the transformation of portfolio composition as well as the associated risk level. The purpose of this study is to provide an in-depth understanding of the impact of the boycott on the investment portfolio structure of related companies on the American Stock Exchange. The result of this research is that there is a change in the allocation of assets held against stocks before the boycott and after the boycott. This research is expected to provide useful insights for investors, financial analysts, and other stakeholders in managing their investment portfolios, especially in anticipating and adjusting investment strategies amid dynamic changes in the stock market.]]>
<![CDATA[Analysis of the Impact of High Inflation on Present Value Calculation in Investment in Indonesia ]]>
<![CDATA[Azzahra, Syanna Nabila]]>;
<![CDATA[Widiana, Fani Almira]]>;
<![CDATA[Azzahra, Fathimah]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.828
<![CDATA[Inflation is a crucial economic indicator that significantly impacts investment decisions. In Indonesia, inflation has shown substantial fluctuations due to factors such as global commodity prices, monetary policy, and domestic demand. This study aims to analyze the impact of high inflation on present value (PV) calculations in investment contexts, particularly in long-term projects. Present value, a method for assessing future cash flows based on their current value, is influenced by the discount rate, which tends to rise with inflation. Using data from 20152023, this research compares two inflation scenarios (1.56% and 6.38%) and calculates the PV of an investment with a future value of IDR 1,000,000 over 5 years. The results show a significant decrease in PV under high inflation, from IDR 925,497 to IDR 733,999, indicating that inflation erodes the purchasing power of future cash flows. Furthermore, the analysis highlights the more significant impact of inflation on sectors with higher cash flow projections, such as infrastructure. The study underscores the need for investors to consider inflation when making investment decisions to manage risks and maximize returns.]]>
<![CDATA[Determining the Pure Premium at Jasa Raharja Insurance Company Purwakarta Branch using Fast Fourir Transform (FFT) through Estimated Aggregate Loss Distribution ]]>
<![CDATA[Saefullah, Rifki]]>;
<![CDATA[Ibrahim, Riza Andrian]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.815
<![CDATA[Insurance is a contractual agreement between two parties, namely the insured party (customer) and the insurer (insurance company), in which the insured party pays a premium to the insurer, then in return, the insurer will provide compensation (claim) to the insured party if an insured event occurs. Each customer is required to pay a premium as an obligation stated in the insurance agreement by paying a premium, the customer fulfills his obligations and is entitled to the benefits stated in the policy. Therefore, the Insurance Company needs to carry out a scheme in the process of paying pure premiums for the sustainability of the insurance company. When determining the premium, it is done by estimating the aggregate loss distribution. This research will calculate thepure premium at the Purwakarta Branch of Jasa Raharja Insurance Company. The model used in this study is the distribution of aggregate loss with a compound distribution of claim frequency and claim size. Many claims follow the Poisson distribution and large claims follow the Lognormal distribution. In the process of estimating the probability of aggregate loss with the compound distribution model, the Inverse method with the Fast Fourier Transform (FFT) algorithm is used. This research will provide understanding and insight to insurance companies in determining the amount of premium that must be charged to customers.]]>
