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Mathematical Modeling of Pulling Force in Tug of War Competitions: A Tribute to Indonesia's Independence Anniversary Pirdaus, Dede Irman; Laksito, Grida Saktian; Hidayana, Rizki Apriva
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.754

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.

Implementation of Ruin Probability Model in Life Insurance Risk Management Lianingsih, Nestia; Hidayana, Rizki Apriva; Saputra, Moch Panji Agung
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.816

This study examines the implementation of the ruin probability model in risk management in life insurance companies. The main focus of this study is to evaluate how factors such as initial surplus, premium revenue level, and claim frequency affect the ruin probability of insurance companies. Using the collective risk model approach and relevant claim distribution, this study develops two methods to calculate the ruin probability: an analytical approach and a Monte Carlo simulation. The simulation results show that increasing the initial surplus and premium level significantly reduces the ruin risk, while increasing the claim frequency increases the ruin probability. In addition, the gamma claim distribution is more suitable for modeling claims in life insurance than the exponential distribution. Model validation is carried out by comparing the prediction results with historical data of insurance companies, which shows a high level of accuracy. This study provides important insights for insurance companies in designing more effective and optimal risk management strategies.

Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
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.v6i2.1020

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.

Optimization Model in Transportation Based on Linear Programming Manuela, Angellyca Leoni; Harahap, Reivani Putri Berlinda; Yoefitri, Tina; Meizani, Nicko; Hidayana, Rizki Apriva
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.v6i2.1018

This study discusses the development of optimization models in transportation costs and routes and resource distribution based on Linear programming using various methods. This study aims to improve logistics efficiency, maximize the utilization of transportation equipment, infrastructure, operations management, and minimize transportation costs. The methods used include data collection, data processing, and the application of mathematical models to determine the optimal route with iteration methods such as the Simplex Method or Simplex Algorithm (SIMPLEKS), Modified Distribution Method (MODI), Vogel's Approximation Method (VAM), North-West Corner Method, Least Cost Method, and Initial Cost Minimum Method (ICMM). This study successfully shows that this method is able to reduce the cost of reducing carbon emissions, significantly reduce shipping costs and increase the efficiency of goods distribution that can be applied to complex distribution systems, support efficiency, and sustainability of transportation management. Using Linear programming and transportation methods to reduce SME costs and produce more efficient costs and fast solutions. In general,optimizationThis supports economic development, efficiency and sustainability of transportation management.

Implementation of Ruin Probability Model in Life Insurance Risk Management Lianingsih, Nestia; Hidayana, Rizki Apriva; Saputra, Moch Panji Agung
International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.816

This study examines the implementation of the ruin probability model in risk management in life insurance companies. The main focus of this study is to evaluate how factors such as initial surplus, premium revenue level, and claim frequency affect the ruin probability of insurance companies. Using the collective risk model approach and relevant claim distribution, this study develops two methods to calculate the ruin probability: an analytical approach and a Monte Carlo simulation. The simulation results show that increasing the initial surplus and premium level significantly reduces the ruin risk, while increasing the claim frequency increases the ruin probability. In addition, the gamma claim distribution is more suitable for modeling claims in life insurance than the exponential distribution. Model validation is carried out by comparing the prediction results with historical data of insurance companies, which shows a high level of accuracy. This study provides important insights for insurance companies in designing more effective and optimal risk management strategies.

Optimization Model in Transportation Based on Linear Programming Manuela, Angellyca Leoni; Harahap, Reivani Putri Berlinda; Yoefitri, Tina; Meizani, Nicko; Hidayana, Rizki Apriva
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.v6i2.1018

This study discusses the development of optimization models in transportation costs and routes and resource distribution based on Linear programming using various methods. This study aims to improve logistics efficiency, maximize the utilization of transportation equipment, infrastructure, operations management, and minimize transportation costs. The methods used include data collection, data processing, and the application of mathematical models to determine the optimal route with iteration methods such as the Simplex Method or Simplex Algorithm (SIMPLEKS), Modified Distribution Method (MODI), Vogel's Approximation Method (VAM), North-West Corner Method, Least Cost Method, and Initial Cost Minimum Method (ICMM). This study successfully shows that this method is able to reduce the cost of reducing carbon emissions, significantly reduce shipping costs and increase the efficiency of goods distribution that can be applied to complex distribution systems, support efficiency, and sustainability of transportation management. Using Linear programming and transportation methods to reduce SME costs and produce more efficient costs and fast solutions. In general,optimizationThis supports economic development, efficiency and sustainability of transportation management.

Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
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.v6i2.1020

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.

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