PERBANDINGAN INTERPOLASI DAN EKSTRAPOLASI NEWTON UNTUK PREDIKSIDATA TIME SERIES - Lamabelawa, Marinus Ignasius Jawawuan (STIKOM Uyelindo Kupang, 2019)
For numerous purposes, time series data are analyzed to understand phenomena or behaviors of variables, and try to find future value. Interpolation is guessing time series data point between the range of data set. Extrapolation is predict or guessing time series data point from beyond the range of data set. In this study, Newton’s Extrapolation is compared with linear and squared extrapolation. Newton’s Extrapolation making the assumption that the observed trend continues for values of x outside the model range. The robustness of prediction using Root Mean Square Error (RMSE) and Mean Average Percentage Error (MAPE). The results of newton’s interpolation with bottom, middle, and top approaches found the best value are middle approach, namely RMSE 76,01 and MAPE 4,65%. In Newton’s Extrapolation, the error values are consistent at bottom, middle, and top approaches, namely RMSE 541,170 anda MAPE 33,19%. Based on data from the Statistics of Indonesia on the percentage and number of poor people in East Nusa Tenggara Province in 2010 -2018 is declining trend pattern. The error value with Linear, Quadratic, and Newton’s Extrapolation shows the robust value results at linear or trend extrapolation, namely RMSE 157,450 and MAPE 7,93%. These results indicate Newton's extrapolation works well on non-linear data and requires a combination method with soft computing methods such as Fuzzy Systems, AG, or ANN