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- Title
Polynomial Interpolation and Cubic Spline to Determine Approximate Function of COVID-19 Tweet Dataset.
- Authors
Munandar, Devi; Suryaningrat, Wahyu; Purwani, Sri
- Abstract
As we know theoretically if we are going to construct a polynomial interpolation function through a mapped base, we create an approximation function. In this study, we try to build an approximation function using all sample data available. The approximation function obtained represents the data whose graph goes through a given set of data points. We determine the value of a function at different points and specific intervals using the interpolation model. The first derivative of the function is obtained to find the growth rate of tweet data. The experimental data is a crawling tweet with the keyword COVID-19. Then we get the amount of data per time duration representing a value of the function at a node. The interpolation includes such as Lagrange, Newton's divided difference, and cubic spline. In this study, we compared polynomial interpolation with cubic splines to obtain optimal results. With the functional approach obtained, a pattern of tweets related to COVID-19 can be seen from its graph that passes through the given data points. The graph and the estimated values obtained show that the cubic spline is the optimal interpolation as an approximation function.
- Subjects
INTERPOLATION; COVID-19; SPLINES; POLYNOMIALS; DERIVATIVES (Mathematics); SPLINE theory
- Publication
IAENG International Journal of Applied Mathematics, 2022, Vol 52, Issue 1, p35
- ISSN
1992-9978
- Publication type
Article