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 K-Means and K-Medoids Clustering in Retail Store Product Grouping ]]>
<![CDATA[Muthmainah, Sekar Ghaida]]>;
<![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.v5i3.753
<![CDATA[The retail business is growing very rapidly with increasing business competition. The application of information technology is one strategy for understanding consumer product purchasing patterns and grouping sales products. This research aims to analyze and compare the K-Means and K-Medoids Clustering techniques for retail data based on the Davies Bouldin Index value and computing time. K-Means is an algorithm that divides data into k clusters based on centroids, while K-Medoids Clustering uses objects with medoids representing clusters as centroid centers. Clustering in both methods produces an optimal number of clusters of 3 clusters. The results of this research show that K-Means produced 358 data in Cluster 1, 292 data in Cluster 2, and 367 data in Cluster 3 with a DBI of 0.7160. Meanwhile, K-Medoids produced 295 data in Cluster 1, 360 data in Cluster 2, and 362 data in Cluster 3 with a DBI of 0.7153. In addition, this study calculated the average computation from 5 experiments, namely K-Means with an average time of 0.024278/s and K-Medoids of 0.05719/s. Based on the lower DBI, K-Medoids have better results in clustering, but the K-Means method is better in terms of computational efficiency. It is hoped that the results of this research will provide valuable insights for retail business people in analyzing sales data.]]>
<![CDATA[Application of the Leslie Matrix on Female Birth Rates and Life Expectancy in the Special Region of Yogyakarta ]]>
<![CDATA[Lianingsih, Nestia]]>;
<![CDATA[Haq, Fadiah Hasna Nadiatul]]>;
<![CDATA[Audina, Yurid]]>
<![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.v5i3.761
<![CDATA[This study aims to predict the number and growth rate of the female population in the Special Region of Yogyakarta for 2025, using the Leslie Matrix model. The matrix utilizes fertility rates and female life expectancy across different age intervals. The data used includes the female population from 2015 and 2020, alongside Age-Specific Fertility Rate (ASFR) data for the same period. By applying the dominant eigenvalue of the Leslie matrix, the study finds that the growth rate of the female population in Yogyakarta is projected to increase, with a dominant eigenvalue of 1.252. The female population is predicted to reach 2,409,852 by 2025, an increase from 1,983,800 in 2020. These findings are expected to inform population management and development planning in Yogyakarta.]]>
<![CDATA[Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method ]]>
<![CDATA[Pamungkas, Abram Chandra Aji]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Susanti, Dwi]]>
<![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.v%vi%i.594
<![CDATA[Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.]]>
<![CDATA[Semantic Classification of Sentences Using SMOTE and BiLSTM ]]>
<![CDATA[Tanjung, Irvan]]>;
<![CDATA[Ilyas, Rid]]>;
<![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.v5i3.750
<![CDATA[A paraphrase is a sentence that is re-expressed with a different word arrangement without changing its meaning (semantics). To find out the semantic proximity to the pair of citation sentences in the form of paraphrases, a computational model is needed. In doing classification sometimes appears a problem called Imbalance Class, which is a situation in which the distribution of data of each class is uneven. There are class groups that have less data (minorities) and class groups that have more data (majority). Any unbalanced real data can affect and decrease the performance of classification methods. One way to deal with it is using the SMOTE method, which is an over-sampling method that generates synthesis data derived from data replication in the minority class as much as data in the majority class. The study applied SMOTE in the classification of semantic proximity of citation pairs, used Word2Vec to convert words into vectors, and used the BiLSTM model for the learning process. The research was conducted through 8 different scenarios in terms of the data used, the selection of learning models, and the influence of SMOTE. The results showed that scenarios using previous research data with BiLSTM and SMOTE models provided the best accuracy and performance.]]>
<![CDATA[Sentiment Analysis of Maxim App User Reviews in Indonesia Using Machine Learning Model Performance Comparison ]]>
<![CDATA[Saefullah, Rifki]]>;
<![CDATA[Yohandoko, Setyo Luthfi Okta]]>;
<![CDATA[Prabowo, Agung]]>
<![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.v5i3.762
<![CDATA[User reviews can vary widely in language and writing style, which can make accurate sentiment modeling difficult. Selecting the right machine learning model and comparing performance between models can be challenging, given that each model has its own strengths and weaknesses. The method used involved data collection by scraping 5000 reviews from the Google Play Store, followed by data pre-processing including data cleaning, tokenization, stemming, and feature engineering using TF-IDF. The data was divided into training (70%) and testing (30%) sets, with the SMOTE oversampling technique applied to address class imbalance. Three machine learning models were used: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The results showed that the majority of reviews were positive, with a high average app rating. Word cloud analysis revealed that “service”, “driver”, “price”, and “time” were the most frequently discussed aspects in the reviews. In terms of model performance, SVM performed the best with an accuracy of 91.3%, followed by Random Forest (89%) and Naive Bayes (78%). Maxim was generally well received by users in Indonesia, with the majority of reviews being positive. The SVM model proved to be the most effective in classifying review sentiment, outperforming other models in accuracy and precision.]]>
<![CDATA[Actuarial Pension Fund Using the Projected Unit Credit (PUC) Method: Case Study at PT Taspen Cirebon Branch Office ]]>
<![CDATA[Amalia, Hana Safrina]]>;
<![CDATA[Subartini, Betty]]>;
<![CDATA[Sukono, Sukono]]>
<![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.v5i3.745
<![CDATA[The pension fund program is a program held by the government to ensure the welfare of Civil Servants (PNS) in retirement as old-age security. The pension program for civil servants is managed by a pension fund, PT Taspen (Persero). Actuarial calculations of pension funds need to be carried out to determine the amount of normal contributions and actuarial liabilities that must be paid by pension plan participants and companies. The actuarial calculation of pension funds used by PT Taspen in managing civil servant pension funds is the Accrued Benefit Cost which determines in advance the benefits that will be obtained by participants. The Projected Unit Credit (PUC) method is one part of the Accrued Benefit Cost. This study aims to determine normal contributions and actuarial liabilities using the Projected Unit Credit (PUC) method for civil servant pension program participants of PT Taspen (Persero) Cirebon Branch Office. The calculation results show that the PUC method provides a more accurate calculation of the estimated normal contributions and actuarial liabilities of the company. This study is expected to be a reference for other companies in managing employee pension funds using an actuarial approach.]]>
<![CDATA[Mathematical Modeling of Pulling Force in Tug of War Competitions: A Tribute to Indonesia's Independence Anniversary ]]>
<![CDATA[Pirdaus, Dede Irman]]>;
<![CDATA[Laksito, Grida Saktian]]>;
<![CDATA[Hidayana, Rizki Apriva]]>
<![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.v5i3.754
<![CDATA[Tug of war is a folk game that uses a mining tool (rope). How to play a team with 2 teams facing each other. Each team consists of 3 or more people, who face each other holding the mine to be pulled. This tug-of-war competition activity is to train body strength, teamwork and cohesiveness. Once the second mark on the rope from the center red mark crosses the center line, the team that pulls the rope to their area wins the game. In this tug of war game there are many styles, including: Frictional Force, Tensile Force, Gravitational Force, and Muscular Force. This paper aims to study the physical forces of tug of war with a mathematical model based on the physical phenomena that exist in the game of tug of war. This model is created by considering tug of war as two objects connected by a rope. The analysis is done by considering the forces acting in the model. The results show that if after being pulled with a force F, the object moves to the right with an acceleration of a, then the acceleration of the object is based on the equation of motion according to Newton's law.]]>
<![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[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.]]>
