TY  - THES
N1  - Sri Utami Zuliana., S.Si.,M.Sc., Ph.D,
ID  - digilib46948
UR  - https://digilib.uin-suka.ac.id/id/eprint/46948/
A1  - DIWANTI PANCA SATITI, NIM.17106010049
Y1  - 2021/08/13/
N2  - Linear regression modelshave some assumptions.One of them is the absence of multicollinearity. If there is a multicollinearity problem, there are several procedures that can be used to overcome the multicollinearity problem. Ridge regression models are one of models could be applied for a multicollinear dataset. In this research, selecting the best model for ridge regression will be applied using Bayesian Information Criterion (BIC) and Schall's Algorithm. Schall?s algorithm begins with choosing any initial penalty weight to estimate the ridge regression coefficient.While BIC is calculated from some penalty weights and the best model has the smallest BIC. The results of this study obtained that the Schall?s algorithm is more effective for optimizing the model than BIC. MSE and MAPE of the Schall?s algorithm smaller than MSE and MAPE of the BIC.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Schall's Algorithm
KW  -  BIC
KW  -  MAPE
KW  -  MSE
KW  -  GRDP
KW  -  Central Java Province
KW  -  Ridge Regression
M1  - skripsi
TI  - PERBANDINGAN OPTIMALISASI MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BIC (Studi Kasus: Model Regresi Ridge untuk Produk Domestik Regional Bruto (PDRB) Provinsi Jawa Tengah)
AV  - restricted
EP  - 88
ER  -