Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Musamus Journal of Public Administration
Ashwar Anis
Universitas Peradaban
Humanities Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Tingkat Keberhasilan Provinsi di Indonesia dalam Kinerja Penanganan Korban Covid 19 Didasarkan Pada Uji Logistic Regression Endro Tri Susdarwono; Ashwar Anis
Musamus Journal of Public Administration Vol 4 No 1 (2021): Musamus Journal Of Public Administration
Publisher : Department of State Administration - Musamus University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35724/mjpa.v4i1.3783

Abstract

Regarding efforts to prevent the spread of the Covid-19 Virus, local governments can take tactical steps and quick action as a matter of anticipation. This study intends to provide a description of the level of success of provinces in Indonesia in handling covid 19 victims based on logistic regression tests. Logistic regression is used to analyze where the assumption of multivariate normal distribution cannot be fulfilled because the independent variable is a mixture of continuous (metric) and categorical (non-metric) variables. In this case, it can be analyzed with logistic regression because there is no need to assume data normality on the independent variables. The conclusion of this study is that based on the logistic regression equation it can be seen that the log of odds for the province to be successful is positively related to provincial performance (KP) and negatively related to provincial size (SIZE). The relationship between the odds and the independent variable can be explained as follows: if KP is considered constant, then the provincial odds will be successfully decreased by a factor of 0.634 (e-0.456) for each unit of increase in SIZE. So KP is considered constant, so the odds for a province to be successful are 0.634 times lower for a densely populated province than for a less populous province. While other variables are considered constant, the odds for the province will be successfully increased by a factor of 5.428 (e1.692) for each unit change in KP. Judging from the classification matrix with a cutoff of 50%, the overall classification rate was 56.25%
Tingkat Keberhasilan Provinsi di Indonesia dalam Kinerja Penanganan Korban Covid 19 Didasarkan Pada Uji Logistic Regression Endro Tri Susdarwono; Ashwar Anis
Musamus Journal of Public Administration Vol 4 No 1 (2021): Musamus Journal Of Public Administration
Publisher : Department of State Administration - Musamus University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35724/mjpa.v4i1.3783

Abstract

Regarding efforts to prevent the spread of the Covid-19 Virus, local governments can take tactical steps and quick action as a matter of anticipation. This study intends to provide a description of the level of success of provinces in Indonesia in handling covid 19 victims based on logistic regression tests. Logistic regression is used to analyze where the assumption of multivariate normal distribution cannot be fulfilled because the independent variable is a mixture of continuous (metric) and categorical (non-metric) variables. In this case, it can be analyzed with logistic regression because there is no need to assume data normality on the independent variables. The conclusion of this study is that based on the logistic regression equation it can be seen that the log of odds for the province to be successful is positively related to provincial performance (KP) and negatively related to provincial size (SIZE). The relationship between the odds and the independent variable can be explained as follows: if KP is considered constant, then the provincial odds will be successfully decreased by a factor of 0.634 (e-0.456) for each unit of increase in SIZE. So KP is considered constant, so the odds for a province to be successful are 0.634 times lower for a densely populated province than for a less populous province. While other variables are considered constant, the odds for the province will be successfully increased by a factor of 5.428 (e1.692) for each unit change in KP. Judging from the classification matrix with a cutoff of 50%, the overall classification rate was 56.25%
Co-Authors Endro Tri Susdarwono