STUDI KETEPATAN TAKSIRAN BERAT JANIN BERDASARKAN STATISTIK DAN TINGGI FUNDUS UTERI

Abstract

Neonatal mortality and maternal mortality when she born still increases because of many factors. One of them is baby born with low weights, this danger potential can be avoid early, if their first weight detection can be done accurately, so the effort of early repairmen for baby weight can be done. This research aims to compare whether two approaches of TBJ and Statistic considerations have similar effectiveness to estimate new baby weight by using the exist newborn baby weight.The kind of this research is by using survey method and cross sectional approach. This research population is all mothers who have ever born baby in the year of 2008. Based on the preface survey, the result is that the number of population is 100 bearing mothers. The analysis method in this research used Regression method. The known research results: 1) The weight of  baby based on height, fundus uteri 3,35 kilograms (95%) the lowest baby weight 2,32 kilograms (1%) and the highest is 4,03 kilograms (4%), 2) Baby weight with statistic model 3,1566 kilograms (95%), the lowest baby weight is 2,23 kilograms (2%) and the highest weight is 3,89 kilograms (3%), 3) The real baby weight that is less than 2,5 kilograms (4%), the normal weight is 3,16 kilograms (96%), 4) The smallest difference is by using statistic approach 0,0002, while approach theory of fetus Weight Appraisal is 0,1930. The accuracy of prediction was done by comparing the average difference between three values such as between the real baby weights with new baby weight prediction with the approach of  baby weight, Difference between newborn baby weight with newborn baby weight from prediction accuracy statistic is decided from the lowest difference, because the estimation value using statistic model has the lowest difference compared with TFU approach, therefore in this case, statistic approach is better.

Keywords :           Baby weight appraisal based on fundus uteri height, fetal weight based on statistic.