PERBANDINGAN OPTIMISASI MODEL REGRESI B-SPLINE MENGGUNAKAN METODE AKAIKE INFORMATION CRITERION(AIC) DAN METODE GENERALIZED CROSS VALIDATION(GCV) (STUDI KASUS : INFLASI KOTA YOGYAKARTA PERIODE JANUARI 2018 - JANUARI 2023)

Ibnu Fadilah, NIM.: 19106010020 (2023) PERBANDINGAN OPTIMISASI MODEL REGRESI B-SPLINE MENGGUNAKAN METODE AKAIKE INFORMATION CRITERION(AIC) DAN METODE GENERALIZED CROSS VALIDATION(GCV) (STUDI KASUS : INFLASI KOTA YOGYAKARTA PERIODE JANUARI 2018 - JANUARI 2023). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Predicting future inflation can be done by creating a model based on historical inflation data. Inflation data is one of the time series data that usually has a certain model. One way to model time series data is to use nonparametric regression. One of the estimation techniques in nonparametric regression is B-Spline estimation. B-Spline method has a function, with the basis of functions that are commonly used, one of the reasons for using the B-Spline regression is because it has good flexibility and the degree of the polynomial does not depend on the control point but depends on the order of the polynomial, which means if you change the data at one point the knots won’t change all the curves. After getting the combination of order and knot points, the researcher optimizes to get the optimal order and knot point. There are many methods that can be used to get the optimal combination. In this study, researchers used the Akaike Information Criterion (AIC) and Generalize Cross Validation (GCV). After getting the optimal order and knot point, we will get an estimation model for the B-Spline. To get the optimal model, the researcher uses the Mean Absolute Prediction Error (MAPE), which has a minimum value. By using D.I Yogyakarta year-on-year inflation data from January 2018 – January 2023, the optimal models in this study are on the order of 4 (cubic) with 4 knots, with AIC value 21,53983 and MAPE value 54,48514 optimal model that obtained is: ˆ Y = (0.1810162)N−3,4(x) + (0.08516874)N−2,4(x) + (0.455261)N−1,4(x) + (0.455261)N0,4(x)

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