ROYHANA DEVI, NIM. 17106010051 (2021) PEMILIHAN MODEL TERBAIK REGRESI RIDGE TINGKAT LAJU INFLASI INDONESIA TAHUN 2019 MENGGUNAKAN METODE ALGORITMA SCHALL DAN AKAIKE’S INFORMATION CRITERION (AIC). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.
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Text (PEMILIHAN MODEL TERBAIK REGRESI RIDGE TINGKAT LAJU INFLASI INDONESIA TAHUN 2019 MENGGUNAKAN METODE ALGORITMA SCHALL DAN AKAIKE’S INFORMATION CRITERION (AIC))
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Text (PEMILIHAN MODEL TERBAIK REGRESI RIDGE TINGKAT LAJU INFLASI INDONESIA TAHUN 2019 MENGGUNAKAN METODE ALGORITMA SCHALL DAN AKAIKE’S INFORMATION CRITERION (AIC))
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Abstract
Multiple linear regression analysis is a regression analysis conducted to see the effect of two or more independent variables on the dependent variable. Multicollinearity is one of the violations in the linear model where there are several methods that can overcome, one of which is using ridge regression. The purpose of this study is to compare the effectiveness of selecting the best model on ridge regression, the Schall Algorithm and Akaike's Information Criterion (AIC) method by comparing the MSE and MAPE values where the smaller the MSE and MAPE values, the better the model is formed. The Schall Algorithm and AIC methods are applied to the Combined Indonesian Inflation Rate of 82 Cities by Spending Group in 2019 data. This data has 1 dependent variable and 8 independent variables. The results of the study state that the MSE and MAPE values from the AIC method are smaller than the Schall algorithm which show that the ridge regression method using AIC is better in overcoming the multicollinearity of data on the Combined Indonesian Inflation Rate of 82 Cities by Spending Group in 2019.
| Item Type: | Thesis (Skripsi) |
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| Additional Information: | Pembimbing : Sri Utami Zuliana, S. Si., M. Sc., Ph. D |
| Uncontrolled Keywords: | Multicollinearity, Ridge Regression, Schall Algorithm, AIC, MSE, MAPE. |
| Subjects: | Matematika |
| Divisions: | Fakultas Sains dan Teknologi > Matematika (S1) |
| Depositing User: | Drs. Mochammad Tantowi, M.Si. |
| Date Deposited: | 06 Dec 2021 14:40 |
| Last Modified: | 06 Dec 2021 14:40 |
| URI: | http://digilib.uin-suka.ac.id/id/eprint/46949 |
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