PEMODELAN REGRESI DERET FOURIER DALAM REGRESI NONPARAMETRIK MULTIVARIABEL (Studi Kasus: Data Curah Hujan Menurut Bulan di Provinsi Jawa Barat Tahun 2015-2019)

ANATANSYAH AYOMI ANANDARI, NIM 17106010029 (2021) PEMODELAN REGRESI DERET FOURIER DALAM REGRESI NONPARAMETRIK MULTIVARIABEL (Studi Kasus: Data Curah Hujan Menurut Bulan di Provinsi Jawa Barat Tahun 2015-2019). Skripsi thesis, FAKULTAS SAINS DAN TEKNOLOGI.

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Abstract

Regression analysis is a statistical analysis of the science used to investigate the pattern of the functional relationship between the two or more variables. One of the nonparametric regression model approaches developed by several researchers is to use the Fourier series. The Fourier series is a trigonometric polynomial that has the flexibility, so that it can adjust effectively to the local properties of the data. This Fourier series estimator is generally used when the data used to investigate the pattern is unknown and there is a trend towards a seasonal pattern. In this study, the authors will examine the nonparametric regression model of the Fourier series which is estimated using the Ordinary Least Square (OLS) method. Determination of the optimal K (Fourier Coefficient) using GCV (Generalized Cross Validation) and MSE (Mean Square Error). In this study, the selection of the best nonparametric regression model for the Fourier series uses the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) criteria which have a minimum value. Nonparametric regression using the Fourier series approach was applied to Rainfall data in West Java Province in 2015-2019. The independent variables used are the average air humidity, air pressure, wind speed, and air temperature. The best nonparametric regression model for the Fourier series is K = 13 which is obtained based on the AIC value of 101.7284 and the BIC value of 221.1061 where the GCV, MSE, and R2 values are 549.92, 462.09, and 97, respectively 30%.

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