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All Journal International Journal of Global Operations Research
Rizky Ashgi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Prediction of the Chances of Death in Covid-19 Data Using the Poisson Process Rizky Ashgi; Sudradjat Supian; subiyanto subiyanto
International Journal of Global Operations Research Vol 1, No 4 (2020)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v1i4.47

Abstract

Covid-19 has brought about major changes for all people in various countries, for example creating vaccines, wearing masks and predicting the predictive state of death that will occur. In this paper, we will predict cases of covid-19 deaths using data taken from the worldometer website by taking data on daily covid-19 deaths worldwide in the period January 23rd- April 16th, 2020. Then the data is processed using the Poisson process that has been transformed using SPSS computer programming, namely the daily mortality rate in the period January 23rd - March 16th, 2020 using descriptive statistics, it was found that the death rate was 4 people in one day, then the Kolmogorov test followed the Poisson distribution, because it met the requirements for the P-value. value . Furthermore, it is calculated by using the death process, which is the chance of an event with the chance of death of all the corona suspects in the next 5 days, namely April 21 because the data has been transformed, so . the chance that no one will die within the next 45 days, namely April 30th, 2020 is close to. In the period of January  23rd  - April 16th, 2020 using descriptive statistics, it was found that the death rate was 6 people in one day, then the Kolmogorov test was carried out with the results following the Poisson distribution, because it fulfilled the requirements for a P-value . Furthermore, it is calculated using the death process, which is the chance of an event with the chance of death of all the corona suspects in the next 5 days, namely April 21st, 2020  because the data has been transformed, so . The chance that no one will die within the next 45 days, namely May 31, is close to .
Order Runge-Kutta with Extended Formulation for Solving Ordinary Differential Equations Wahyu Suryaningrat; Rizky Ashgi; Sri Purwani
International Journal of Global Operations Research Vol 1, No 4 (2020)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v1i4.61

Abstract

The mathematical model has been used to understand many phenomena and natural interactions. Since including many variables and parameters, the complex models are not easy to find analytical solutions. In this paper, we analyze one of the family of Runge–Kutta method with an expansion of evaluation function. We applied the proposed method to solve ordinary differential equations problems and compared it with other well-known Runge-Kutta methods. The computation cost and accuracy for each method have been analyzed.
A Bibliometric Analysis for Lebesgue Measure Integration in Optimization Endang Rusyaman; Devi Munandar; Diah Chaerani; Dwindi Agryanti Johar; Rizky Ashgi
International Journal of Global Operations Research Vol 2, No 2 (2021)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v2i2.58

Abstract

In solving mathematical problems so far, Riemann's integral theory is quite adequate for solving pure mathematics and applications problems. But not all problems can be solved using this integration, such as a discontinuous function that is not Riemann's integration. Lebesgue integral is an integration concept based on measure and can solve finite and unlimited function problems and be solved in a more general set domain. One of the bases of this integration is the Lebesgues measure includes the set of real numbers, where the length of the interval is the endpoints. The alternative use of this integral is widely used in various studies such as partial differential equations, quantum mechanics, and probabilistic analysis, requiring the integration of arbitrary set functions. This paper will show a comprehensive bibliometric survey of peer-reviewed articles referring to Lebesgue measure in integration. Search results are obtained 832 papers in the google scholar database and 997 papers using Lebesgue measure integration in optimization. It can also be seen that the research have 4 clusters and 3 clusters respectively with scattered keywords for each cluster. Finally, using bibliographic data can be obtained Lebesgues measure in integration and optimization supports many of the research and provides productive citations to citing the study.
Co-Authors Devi Munandar Diah Chaerani Dwindi Agryanti Johar Endang Rusyaman Sri Purwani Subiyanto Subiyanto Sudradjat Supian Wahyu Suryaningrat