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Journal : International Journal of Quantitative Research and Modeling

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

Abstract

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.
Best Distribution Selection in Modeling the Interest Rate as a Random Modifier Kusumawati, Fajry Ayu; Prabowo, Agung; Br. SB, Agustini Tripena; Laksito, Grida Saktian
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.v5i2.683

Abstract

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.