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Soenarnatalina Meilanani

Faculty of Public Health, Airlangga University

Author-ID : 2535483

Dentistry Health Professions Medicine & Pharmacology Nursing Public Health

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Bagging Approach for Increasing Classification Accuracy of CART on Family Participation Prediction in Implementation of Elderly Family Development Program Wisoedhanie Widi Anugrahanti; Arief Wibowo; Soenarnatalina Meilanani
Health Notions Vol 1, No 2 (2017): April-June
Publisher : Humanistic Network for Science and Technology (HNST)

Classification and Regression Tree (CART) was a method of Machine Learning where data exploration was done by decision tree technique. CART was a classification technique with binary recursive reconciliation algorithms where the sorting was performed on a group of data collected in a space called a node / node into two child nodes (Lewis, 2000). The aim of this study was to predict family participation in Elderly Family Development program based on family behavior in providing physical, mental, social care for the elderly. Family involvement accuracy using Bagging CART method was calculated based on 1-APER value, sensitivity, specificity, and G-Means. Based on CART method, classification accuracy was obtained 97,41% with Apparent Error Rate value 2,59%. The most important determinant of family behavior as a sorter was society participation (100,00000), medical examination (98,95988), providing nutritious food (68.60476), establishing communication (67,19877) and worship (57,36587). To improved the stability and accuracy of CART prediction, used CART Bootstrap Aggregating (Bagging) with 100% accuracy result. Bagging CART classifies a total of 590 families (84.77%) were appropriately classified into implement elderly Family Development program class. Keywords: Bagging Classification and Regression Tree, Classification Accuracy, Family Participation

The Effect of Dayak Onion Bulb-Stem (Eleutherine Palmifolia (L.,) Merr.) Extract on Blood Glucose Levels of Mouse Suffered Diabetes Mellitus Niluh Arwati; Bambang Wirjatmadi; Merryana Adriani; Soenarnatalina Meilanani; Dwi Winarni; Sri Hartiningsih
Health Notions Vol 2, No 3 (2018): March
Publisher : Humanistic Network for Science and Technology (HNST)

Dayak onions (Eleutherine Palmifolia (L.) Merr.) bulb stem contains phtyochemical contents, which act as antidiabetic compounds, such as eleutherol, eleuthocide A, and eleutherinoside B, as well as antioxidant compounds, which include triterpenoid, poliphenol, and flavonoid. Dayak onions was able to be used as the antidiabetic, since it had the ability to lower the blood glucose level and prevent from the free radicals, thus supressing the oxidative stress condition. This research had purpose to analyze the effect of Dayak onions bulbstem as antioxidant and anti diabetic drugs. The research used experimental method with the population in this research was 25 male white mice strain wistar. The concentration of Dayak onions bulb-stem extracts were 300mg.kgBW-1, 400mg.kgBW-1, and 500mg.kgBW-1. Data analysis used Tukey HSD Test with 95% of significance degree and was continued using manova test (average group ratio test). The result showed that. the extract of Dayak onions bulb-stem had the antidiabetical and antioxidant activity, which could lower the blood glucose levels and malondialdehid on the male white mice strain Wistar with the optimum effective doze of 500 mg.kgBW-1.Keywords: Dayak onions bulb-stem, Blood glucose level, Malondialdehid (MDA)

Spatial Modeling of Infant Mortality Rate In South Central Timor Regency Using GWLR Method With Adaptive Bisquare Kernel And Gaussian Kernel Teguh Prawono Sabat; Soenarnatalina Meilanani; Windhu Purnomo
Health Notions Vol 1, No 2 (2017): April-June
Publisher : Humanistic Network for Science and Technology (HNST)

Geographically Weighted Logistic Regression (GWLR) was regression model consider the spatial factor, which could be used to analyze the IMR. The number of Infant Mortality as big as 100 cases in 2015 or 12 per 1000 live birth in South Central Timor Regency. The aim of this study was to determine the best modeling of GWLR with fixed weighting function and Adaptive Gaussian Kernel in the case of infant mortality in South Central Timor District in 2015. The response variable (Y) in this study was a case of infant mortality, while variable predictor was the percentage of neonatal first visit (KN1) (X1), the percentage of neonatal visit 3 times (Complete KN) (X2), the percentage of pregnant get Fe tablet (X3), percentage of poor families pre prosperous (X4). This was a non-reactive study, which is a measurement which individuals surveyed did not realize that they are part of a study, with analysis unit in 32 sub-districts of South Central Timor Districts. Data analysis used open source program that was Excel, R program, Quantum GIS and GWR4. The best GWLR spatial modeling with Adaptive Gaussian Kernel weighting function, a global model parameters GWLR Adaptive Gaussian Kernel weighting function obtained by g (x) = 0.941086 - 0,892506X4, GWLR local models with adaptive Kernel bisquare weighting function in the 13 Districts were obtained g(x) = ????0 − ????0X4, factors that affect the cases of infant mortality in 13 sub-districts of South Central Timor Regency in 2015 was the percentage of poor families pre prosperous. Keywords: Kernel, Adaptive bisquare, GWLR, Infant mortality

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