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
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<![CDATA[Comparative Analysis of the Effectiveness Between Malwarebytes and BitDefender to Prevent Malware Attacks ]]>
<![CDATA[Yohanza, Mohammad Zidan]]>;
<![CDATA[Giat, Muhammad]]>;
<![CDATA[Fadhilah, Muhammad Iksan]]>;
<![CDATA[Sulaeman, Mohammad]]>;
<![CDATA[Iskandar, Ibrahim Dafi]]>;
<![CDATA[Hidayat, Yuyun]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.313
<![CDATA[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.]]>
<![CDATA[Analysis of Pet Owners' Willingness to Pay for Pet Insurance Premiums in DKI Jakarta Using Logistic Regression Model ]]>
<![CDATA[Adib, Andhita Zahira]]>;
<![CDATA[Riaman, Riaman]]>;
<![CDATA[Subartini, Betty]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.578
<![CDATA[Pets provide many benefits to their owners, both physically and mentally. Pet lovers are increasingly aware of the importance of proper health and care for their beloved animals. This has led pet enthusiasts to consider pet insurance. In participating in insurance, there are factors that influence the willingness of pet owners to pay premiums. The objective of this research is to determine the premium for pet insurance and analyze the factors influencing the Willingness To Pay (WTP) of pet owners. This study utilizes choice modeling format by conducting surveys to identify the factors influencing the purchase of pet insurance. Subsequently, binary logistic regression model analysis using the Maximum Likelihood Estimation (MLE) method and the Newton-Raphson Iteration approach is employed to analyze the factors influencing the magnitude of WTP. The research results show that the average willingness to pay for pet insurance premiums is IDR128,574.76 per year. Factors influencing the decision of pet owners include the number of family dependents and awareness of the importance of participating in pet insurance. The likelihood of cat owners being willing to pay pet insurance premiums is 0.8691 or 86.91%.]]>
<![CDATA[Strategizing Financial Triumph: Applying Advanced Mathematical Models to Revolutionize Bond Investments in the Modern Financial Industry ]]>
<![CDATA[Pasha, Raisa Huria]]>;
<![CDATA[Seno, Nathaniela Apdie]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.668
<![CDATA[The importance of applying advanced mathematical models in bond investing marks a revolutionary step in the modern financial industry, enabling more scalable and adaptive strategies to achieve financial success. The purpose of this talk is to explore and detail the role of advanced mathematical models in changing the bond investment paradigm. The discussion aims to highlight the crucial role of advanced mathematical models in changing the bond investment paradigm, providing a deeper understanding of the optimal potential and risks involved, explaining how this approach can optimize financial outcomes through more detailed analysis. The application of mathematical models involves the use of sophisticated algorithms and statistical analysis to identify optimal investment opportunities. These steps include the use of advanced financial math formulas, such as yield to maturity and duration, to design investment strategies that are adaptive and responsive to bond market dynamics. The application of mathematical models results in a deeper understanding of the bond market, allowing investors to respond quickly to changing market conditions. Thus, the investment strategy formed by this approach can not only improve investment returns, but also reduce the risks that investors may face. The application of advanced mathematical models in bond investing opens the door to smarter and more informed decision-making. By combining data and mathematical analysis, investors can maximize potential investment returns and manage risks more effectively.]]>
<![CDATA[Mathematical Model Analysis of Mosaik Disease Spread on Jatropha Plants: Article Review ]]>
<![CDATA[Rahmani, Ayun Sri]]>;
<![CDATA[Subiyanto, Subiyanto]]>;
<![CDATA[Supian, Sudradjat]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.669
<![CDATA[Mosaic disease is one of the plant diseases that can be detrimental and cause crop failure, the disease is caused by Begomovirus. Begomovirus is spread by whitefly vectors. The whitefly as a vector can infect healthy plants because once the whitefly is infected, the whitefly body will forever contain the disease. Therefore, we need a mathematical model to prevent the spread of mosaic disease on Jatropha plants and make a strategy to prevent mosaic disease with optimal control and other factors. In this study, mathematical modeling of the spread of jatropha mosaic disease will be discussed, with the addition of various compartments, parameters, and optimal control. Several strategies that can be used to prevent mosaic disease in Jatropha are adding effect awareness, delay, insecticides, interventions, natural predators, yellow stick, rouging, and a combination of all strategies.]]>
<![CDATA[Prediction of Motor Vehicle Insurance Claims Using ARMA-GARCH and ARIMA-GARCH Models ]]>
<![CDATA[Maraya, Nisrina Salsabila]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.672
<![CDATA[Motorized vehicles are one of the means of transportation used by Indonesian people. As of 2021, the Central Statistics Agency (BPS) recorded the growth of motorized vehicles in Indonesia reaching 141,992,573 vehicles. Lack of control over the number of motorized vehicles results in losses for various parties, such as accidents, damage and other unwanted losses. The size of insurance claims has the potential to fluctuate, because it is influenced by several factors, such as policy changes, market conditions and economic conditions. This research aims to predict the size of motor vehicle insurance claims using the ARMA-GARCH model which is used to predict the size of vehicle insurance claims by dealing with non-stationarity and heteroscedasticity in time series data. Based on research, the best model obtained is the ARMA(3,3)-GARCH(1,0) model which produces nine significant parameters. Meanwhile, based on the MAPE value, it shows that the ARMA(3,3)-GARCH(1,0) model is quite accurate. The results of this research can be taken into consideration in predicting the size of insurance claims in the future.]]>
<![CDATA[Based Stock Valuation Analysis on Fuzzy Logic for Investment Selection (Case Study: PT. XL Axiata Tbk. and PT. Telkom Indonesia Tbk.) ]]>
<![CDATA[Audina, Maudy Afifah]]>;
<![CDATA[Susanti, Dwi]]>;
<![CDATA[Sukono, Sukono]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.673
<![CDATA[The stock value of a company fluctuates with capital market conditions, requiring investors to consider various factors for precise investment decisions. Stock valuation determines the fair price of a company's stock, guiding buying and selling transactions. This research uses Discounted Cash Flow (DCF), Price to Earnings (P/E), and Enterprise Value to EBITDA (EV/EBITDA) to ascertain fair stock prices, integrating results with Mamdani fuzzy logic to determine investment weights. The result of this research is that both EXCL and TLKM hold significant weight in the investment portfolio with TLKM has slightly higher stock weight than EXCL. This suggests TLKM offers more potential for profitable future investments. Investors can use these results in portfolio management for investment selection]]>
<![CDATA[Implementation of Bidirectional Long Short Term Memory (BiLSTM) Algorithm with Embedded Emoji Sentiment Analysis of Covid 19 Anxiety Level and Socio Economic Community ]]>
<![CDATA[Marcelina, Jenie]]>;
<![CDATA[Tosida, Eneng Tita]]>;
<![CDATA[Aryani, Adriana Sari]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.682
<![CDATA[The COVID-19 pandemic has presented multidimensional challenges in Indonesia, significantly affecting social, economic, and public health at the level of anxiety. Public anxiety related to the pandemic can be reflected in online media, especially Twitter, which is the main channel for information sharing and emotional expression. This study aims to understand the level of public anxiety in relation to the aftermath of the COVID-19 pandemic by using a classification method. Classification is carried out using the Knowledge Discovery in Database method with the Bidirectional LSTM algorithm and emoji embedding sentiment analysis, and K-Fold Cross Validation testing is also carried out with various optimizers. The final result of the best accuracy rate obtained was 98.08%. This shows that the classification model created is good.]]>
<![CDATA[Best Distribution Selection in Modeling the Interest Rate as a Random Modifier ]]>
<![CDATA[Kusumawati, Fajry Ayu]]>;
<![CDATA[Prabowo, Agung]]>;
<![CDATA[Br. SB, Agustini Tripena]]>;
<![CDATA[Laksito, Grida Saktian]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.683
<![CDATA[The interest rate is seen as a random variable because the interest rate has an unpredictable nature or changes over time. This means that the interest rate cannot be anticipated in the future with a certain degree of certainty. Therefore, mathematical models are needed to predict the behavior and value of future interest rates. The models used in this study were interest rate, uniform distribution , and lognormal distribution. The data used in the study were interest rate data for 2014-2015 and sample data for uniform distribution. The resulting model in interest rate modeling as a random variable uses for uniform and lognormal distributions with the application of data and . The interest rate model as a uniformly distributed random variable is considered better with a smaller standard deviation, , and values compared to the lognormal distribution based on the data used.]]>
<![CDATA[Comparison of Agricultural Insurance Premium Prices Based on Rainfall Index and Based on Corn Commodity Production Prediction Index in Grobogan District ]]>
<![CDATA[Rosady, Tiara]]>;
<![CDATA[Prabowo, Agung]]>;
<![CDATA[Guswanto, Bambang Herdriya]]>;
<![CDATA[Saputra, Jumadil]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.684
<![CDATA[In running an agricultural business, there is a big risk because farmers' productivity is very dependent on nature, and to reduce the risk of crop failure, the government provides a program to minimize losses from crop failure, namely agricultural insurance, through Law Number 19 of 2013 . This article aims to compare agricultural insurance premium prices based on the rainfall index and the production prediction index for corn commodities in Grobogan Regency. The results of this article are based on the reference used of 3% of compensation, namely IDR 344,872.5, and the results that are close to the reference are the price of insurance premiums based on the 5th simple average method corn production prediction index of IDR 356,433.58 . However, the premium price used as a reference previously was the gross premium price from AUTP, so the insurance premium price for Corn Farmers (AUTJ) was obtained, namely IDR 310,385.25. As a result, we obtained premium price results that were closest to the reference from AUTJ, namely at the 4th percentile, when the agricultural insurance premium price based on the rainfall index was IDR 330,155.76 per planting season.]]>
<![CDATA[Calculation of Motor Vehicle Insurance Premiums by using the Moment Method to Estimate the Aggregate Claim Model ]]>
<![CDATA[Alfaridzi, Sultan Izbik Riska]]>;
<![CDATA[Prabowo, Agung]]>;
<![CDATA[Nurhayati, Nunung]]>;
<![CDATA[Halim, Nurfadhlina Abdul]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i2.685
<![CDATA[The aggregate claim model is a model that can be used to determine the amount of premium billed to the insured by the insurance company. This model consists of a combination of two independent random variables, namely the number of claims that occur and the size of the claim for each claim submitted. In this study, many claims are Poisson distributed and the size of the claim is exponentially distributed. The method of moments is used to estimate the parameters of each distribution. Based on the calculation results, the amount of premium billed to the insured for one year if based on the pure premium principle is Rp. 112,500,000.00 and if based on the expected value principle, variance value principle, and standard deviation principle is Rp. 165,900,000.00.]]>
