Lumbung Pustaka UNY: No conditions. Results ordered -Date Deposited. 2024-03-29T04:51:29ZEPrintshttp://eprints.uny.ac.id/apw_template/images/sitelogo.pnghttps://eprints.uny.ac.id/2015-07-15T21:40:30Z2015-07-15T21:40:30Zhttp://eprints.uny.ac.id/id/eprint/23641This item is in the repository with the URL: http://eprints.uny.ac.id/id/eprint/236412015-07-15T21:40:30ZAUTOLOGISTICS MODELS FOR MODELLING AND MAPPING INFANT MORTALITY IN INDONESIAAccording to Indonesia Demographic and Health Survey (IDHS) data, the highest child mortality occurred during the first year of age of infant. Infant mortality is an important indicator that must to be monitored seriously. The mortality is associated with several determinants, such as the infant’s characteristics, maternal and fertility factors, housing condition, and also geographical area. The aim of this research is to develop models that can be used to explain the effects of explanatory variables on infant mortality in Indonesia. It is also the aim of this research to develop a thematic map describing the distribution pattern of infant mortality probabilities at district level across the country. The response variable is a binary categorical variable with two outcomes, success and failure. The outcome is success if the infant died before achieving one year of age, and failed if the infant is still alive after one year of age. Modeling is using Logistic Regression model and Autologistics Regression model. The results showed that the Autologistics Regression model fitted the data reasonably well, all of the explanatory variable affect infant mortality, except infant’s sex. The results also showed that the probabiity of infant mortality was higher in Kalimantan island and Papua island.
Keyword : Infant, mortality, autologistic, binary, IDHS. Ray Sastri. Khairil Anwar Notodiputro. Indahwati2015-07-15T12:31:09Z2015-07-15T12:31:09Zhttp://eprints.uny.ac.id/id/eprint/23630This item is in the repository with the URL: http://eprints.uny.ac.id/id/eprint/236302015-07-15T12:31:09ZMODELLING CASES OF LOW BIRTH-WEIGHT INFANTS WITH GENERALIZED LINEAR MIXED MODELLow Birth-Weight (LBW) is defined as a birth weight of a live-born infant of less than 2.500 grams regardless of gestational age. The causes of LBW cases can be grouped into two main causes: premature birth and case of small for gestational age (SGA). There are many risk factors that can induce directly or indirectly so that these causes may occur. Case of LBW is associated with infant mortality, infant morbidity, inhibited growth and slow cognitive development, also chronic diseases in later life.
To suppress rate of LBW first we must estimate the rate correctly. Data of LBW comes from Indonesian Health and Demographic Survey (IDHS) 2012 which is divided into 3 groups: written (measured accurately), recall (measured inaccurately) and not weighed (not measured). Published national rate of LBW is 7.3% with provincial rates fall between 4.7-15.7 %. The estimation came from only 2 former groups without consideration of assumed difference accuracy on second group.
To estimate the difference and the rate of the third group, Generalized Linear Mixed Model (GLMM) is used with live-born infants as observation units because observations from the same sampling unit tends to correlate due to multistage sampling design.
The result of the model at α = 0.05 is highly-significant, with fixed effect variables that are statistically significant to the case of LBW are Estimated Size, Preceding Interval, Pregnancy Complication, Mother’s Age, Province and Education. Higher portion of variance component is on the G-side as a result of multistage sampling, with Household level has highest within variance. On the R-side, recall group data has higher variance than written group. It is an indication of lower accuracy of the birth weight data on this group. Based on the model, estimation of LBW rate including not weighed group result 7.96% slightly higher than direct estimate.
Keywords: Low Birth-Weight, GLMM, Logistic Regression, IDHS 2012. Antonius Benny Setyawan. Khairil Anwar Notodiputro. Indahwati2015-07-10T21:05:25Z2015-07-10T21:05:25Zhttp://eprints.uny.ac.id/id/eprint/23325This item is in the repository with the URL: http://eprints.uny.ac.id/id/eprint/233252015-07-10T21:05:25ZACCURACY COMPARISON OF SIMPLE, SYSTEMATIC, AND STRATIFIED RANDOM SAMPLING FOR ESTIMATING POPULATION (MINIMARKET CASE IN INDONESIA)This paper aims to compare the accuracy of three methods of sampling, namely Simple Random Sampling (SRS), Systematic Random Sampling and Stratified Random Sampling. The data used as the simulation is data PODES 2011. So the data that is used as a parameter in this study are primary data in the form of data results PODES 2011 were rural / village with minimarket (RVM). The method of analysis is done in two stages. The first phase saw the condition of the minimarket population data as a basis for exploration. The second stage of the estimation simulate the total population and the variance by using simple random sampling, systematic random sampling, and stratified random sampling. The next stage of comparing the results of estimation of population parameters of the sampling methods to the total population of the minimarket PODES 2011. The results are reviewed based on the accuracy of each method. Stratified random sampling method (DKM as a base coating) with a sample size of 500 generate predicted values with the highest degree of accuracy compared with the SRS and systematic random sampling. Its bias value at 22 outlets, its standard error of 2606.2 outlets, and his MSE of 6.79254 million.
Keywords: accuracy, comparison, simple, systematic, stratified, random. Abadi Wibowo. Indahwati. I Made Sumertajaya. Erni Tri Astuti