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All Journal Jurnal Kedokteran Brawijaya Enthusiastic : International Journal of Applied Statistics and Data Science Bulletin of Applied Mathematics and Mathematics Education
Setyawan, Yudi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Medicine & Pharmacology

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Laboratory Service Process Checklist: Preventive Measures to Increase Blood Glucose Test Accuracy Setyawan, Yudi; Wardhani, Viera
Jurnal Kedokteran Brawijaya Vol. 31 Supplement 2 (2021)
Publisher : Fakultas Kedokteran Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jkb.2021.031.02.15s

Abstract

The accuracy of the blood glucose test results is an essential measurement for hospital quality since it affects the plan, decision, and outcome of the treatment. This study aimed to develop a checklists model to improve the laboratory results accuracy. The checklist development is based on the laboratory services processes covering pre-analytic, analytic, and post-analytic stages, which are implemented in all blood glucose test requests during the study period (65 examinations), the first week of October 2020. The implementation resulted in no incidence of test inaccuracy when conformed with patient clinical information. The staff expressed that completing the checklist is quick and easy to complete (3-5 minutes) and beneficial. The problem occurs when staff works alone, causing delays in completing the checklist. Therefore, regular monitoring and evaluation are suggested to ensure compliance and divide the checklist into two stages. The pre-analytic stage is first carried out for all patients, followed by the analytical and post-analytic stages because the last two activities were located on different floors. In short, checklists are effective as preventive measures to increase the conformity of laboratory examination results with patient clinical information.
Applying negative binomial regression analysis to overcome the overdispersion of Poisson regression model for malnutrition cases in Indonesia Setyawan, Yudi; Suryowati, Kris; Octaviana, Dita
Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v2i2.4948

Abstract

Indonesia is one of the developing countries that is struggling to eradicate malnutrition problem. Malnutrition that occurs over a long period of time can have an impact on deaths for the sufferers and decreasing human’s quality of life. This study aims to model the case of malnutrition that occurred in Indonesia Provinces during 2015, and get the main factors that cause malnutrition problem. Variables studied consists of Malnutrition (Y), Vitamin A consumption (X1), Exclusive breastfeeding (X2), Immunization (X3), Water quality (X4), Healthcare center (X5), and Poverty level (X6). Based on the Kolmogorov-Smirnov test, the results of malnutrition data in Indonesia Province in 2015 does not follow Poisson distribution because of overdispersion. The presence of overdispersion cases in the Poisson regression model will have an impact on the inappropriateness of inferences. An alternative model that can accomodate this case is negative binomial regression model.  By using this model, factors that are considered influencing malnutrition cases in Indonesia provinces in 2015 are Immunization (X3), Water quality (X4), and Poverty level (X6). The best model obtained from negative binomial regression analysis is μ ̂_i=exp(2.5111-0.0338X_3+0.0295X_4+0.0576X_6).
Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province Hasibuan, Devi Octaviani; Pau Teku, Heribertin; Drostela Putri, Maria Fatima; Setyawan, Yudi; Dwi Bekti, Rokhana
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art6

Abstract

Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed.  The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020.  The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.
Co-Authors Drostela Putri, Maria Fatima Dwi Bekti, Rokhana Hasibuan, Devi Octaviani Kris Suryowati, Kris Octaviana, Dita Pau Teku, Heribertin Viera Wardhani