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All Journal Jurnal Wira Ekonomi Mikroskil Agrifor : Jurnal Ilmu Pertanian dan Kehutanan Tanra : Jurnal Desain Komunikasi Visual Fakultas Seni dan Desain Universitas Negeri Makassar Humani (Hukum dan Masyarakat Madani) Jurnal Ius Constituendum JEMST: Journal of Education in Mathematics, Science, and Technology Journal Publicuho MEJ (Mathematics Education Journal) Economic and Education Journal (Ecoducation) Vygotsky: Jurnal Pendidikan Matematika dan Matematika Journal of Education and Learning Mathematics Research (JELMaR) Musamus Journal of Public Administration Journal of Governance and Local Politics (JGLP) Journal of Law and Policy Transformation Dinamika Bahari: Journal of Maritime Dynamic Jurnal Inovasi Pendidikan dan Sains Al-fahim : Jurnal Manajemen Pendidikan Islam Eduproxima : Jurnal Ilmiah Pendidikan IPA Indonesian Journal of Police Studies Jurnal Cafetaria Special and Inclusive Education Journal (SPECIAL) Indonesian Journal of Mathematics and Natural Science Education Jurnal PEKA (Pendidikan Matematika) Jurnal Litbang Kota Pekalongan LINEAR: Journal of Mathematics Education Oikonomia: Jurnal Manajemen Dawuh Guru: Jurnal Pendidikan MI/SD VEKTOR: Jurnal Pendidikan IPA Jurnal Digital Media dan Relationship Experiment: Journal of Science Education Al-Kawakib JPPOL : Jurnal Poros Politik Jurnal Pendidikan Vokasi Raflesia Al Kawnu : Science and Local Wisdom Journal Jurnal Pendidikan LLDIKTI Wilayah I (JUDIK) Jurnal Muassis Pendidikan Dasar Jurnal Hukum dan HAM Wara Sains PLEDOI (Jurnal Hukum dan Keadilan) Sosial Khatulistiwa: Jurnal Pendidikan IPS Quadratic: Journal of Innovation and Technology in Mathematics and Mathematics Education Jurnal El-Hamra : Kependidikan dan Kemasyarakatan Economic and Education Journal (Ecoducation) Indonesian Journal of Mathematics and Natural Science Education
Endro Tri Susdarwono
Universitas Peradaban
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Musamus Journal of Public Administration

 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 Anis, Aswhar Ashwar Anis Elly Kusbandini Firman Sani M. Syamsul Rizal S. Thoriqul Huda