MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) (Studi Kasus : Pengukuran Indeks Profesional Pegawai Puskesmas Guntur II Kabupaten Demak)

ISTINGANATUN, NIM.17106010046 (2021) MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) (Studi Kasus : Pengukuran Indeks Profesional Pegawai Puskesmas Guntur II Kabupaten Demak). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Regression analysis is used to determine the relationship between the independent and dependent variables. To model the regression must meet the classical assumption test. If the data contains multicollinearity, it has violated the assumption test. The ridge regression model is used to overcome the existence of multicollinearity, by adding a bias estimator. In this study the Schall algorithm and Bayesian Information Criterion (BIC) method obtain the best ridge regression model. The purpose of this study is to examine the regression model and the selection of the best model, as well as to determine the comparison of the best model selection method for ridge regression. The problem of multicollinearity in the professional index measurement data of the Guntur II Public Health Center in Demak Regency can be resolved by using the ridge regression model so that the best model is obtained using the Schall Algorithm and BIC methods. The performance results of these two methods are almost the same. If measured by the MSE and MAPE values, the BIC method is better, but when viewed from the speed of running execution, the Schall algorithm method is better. Moreover, the iterations used in the Schall algorithm method are not too many.

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