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Darnah Andi Nohe
Dosen Program Studi Statistika FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pemodelan Status Kesehatan Pasien Medical Check Up Klinik Handil Muara Jawa Dengan Regresi Logistik Biner Rakhmanto Anugrah Darmawan; Darnah Andi Nohe; Desi Yuniarti
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (572.204 KB)

Abstract

Health is a major human needs are also priorities in human life. many types of health providers available to the community as an example of health care clinics were organized promotive, preventive, curative and rehabilitative. Handil clinics serve patients Muara Jawa Medical Check-Up for the workers of the company or the public. To analyze the factors that affect the health of a patient Medical Check Up can use logistic regression analysis. Logistic regression analysis is an analysis that describes the relationship between the response variable is binary with explanatory variables that can be either qualitative or quantitative variables variables. Based on the research results, we concluded that of the testing parameters, only gender and companies that significantly affect the patient's health status.
Penerapan Statistika Nonparametrik dengan Metode Brown-Mood pada Regresi Linier Berganda Ni Wayan Rica A; Darnah Andi Nohe; Rito Goejantoro
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Brown-Mood is a method first developed by GW brown 1950 and AM mood in 1951 with the purpose of the parameters of the multiple linear regression model of the linear regression model of the equation of the median small sample size. This study discusse the application of the method of brown-mood on multiple linear regression with the open unemployment rate (X1), and growth rate of gross regional domestic product at constant prices (X2) to the number of poor population (Y) Province of east Kalimantan. If the method ordinary least square in a multiple linear regression is a statistical parametric aims to minimize the average (mean) error, the brown-mood methods as a nonparametric statistical method chose a multiple linear regression model by minimising the median and average weighted. The results of this research to get a linear regression model using the method of brown-mood is Ŷ=-31.11+1.74 X1 + 1.44 X2 from the multiple linear regression model obtained are percentage distribution of gross regional domestic product at current prices [without oil, gas and its products] and growth rate of gross regional domestic product at constant prices affect to the number of poor population.
Perbandingan Peta Pengendali Rata-rata Bergerak Dengan Peta Pengendali Rata-rata Bergerak Geometrik Nurdayanti Nurdayanti; Darnah Andi Nohe; Rito Goejantoro
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (296.906 KB)

Abstract

The Moving Average Control Chart is a control chart of data observation for small average shift process. The Geometric Moving Average Control Chart is a control chart of specific weight, making it more effective in detecting the smallest change in process. The purpose of this study is to determine whether the wood width which produced by Suryadi Moulding are controlled by Moving Average and Geometric Moving Average Control Chart, and between the two control chart the research want to know which is the best chart.Based on the results of research in the wood width data obtained that the Moving Average and Geometric Moving Average Control Chart there are no points on the outside of the control limits so that it can be concluded that the wood width which produced by Suryadi Moulding Samarinda on the under controlled conditions. If viewed from the width limit controller chart because of the wide limit on the Geometric Moving Average Control Chart is better than the Moving Average Control Chart because the wide limit on the Geometric Moving Average Control Chart is narrower so the result of this control chart is more accurate.
Co-Authors Desi Yuniarti Ni Wayan Rica A Nurdayanti Nurdayanti Rakhmanto Anugrah Darmawan Rito Goejantoro, Rito