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Soenarnatalina Meilanani, Soenarnatalina
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Dentistry Health Professions Medicine & Pharmacology Nursing Public Health

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The Effect of Dayak Onion Bulb-Stem (Eleutherine Palmifolia (L.,) Merr.) Extract on Blood Glucose Levels of Mouse Suffered Diabetes Mellitus Arwati, Niluh; Wijatmadi, Bambang; Adriani, Merryana; Meilanani, Soenarnatalina; Winarni, Dwi; Hartiningsih, Sri
Health Notions Vol 2 No 3 (2018): March 2018
Publisher : Humanistic Network for Science and Technology (Address: Cemara street 25, Ds/Kec Sukorejo, Ponorogo, East Java, Indonesia 63453)

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Abstract

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 bulb-stem 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)
Multivariate Adaptive Regression Spline Approach for the Classification Accuracy of Drugs User in East Java Wenno, Stefanny Zulistya; Kuntoro, Kuntoro; Meilanani, Soenarnatalina
Health Notions Vol 1 No 2 (2017): April-June 2017
Publisher : Humanistic Network for Science and Technology (Address: Cemara street 25, Ds/Kec Sukorejo, Ponorogo, East Java, Indonesia 63453)

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Background : Classification method is a statistical method for grouping or classifying the systematically arranged data into a group so we can know that an individual are in a particular group. Multivariate Adaptive Regression Spline (MARS) introduced by (Friedman, 1991) is a methodology for approximating functions of many input variables given the value of the function at a collection of points in the input space. Although training times for this method tend to be much faster than feed forward neural networks using back propagation, it can still be fairly slow for large problems that require complex approximations (many units). Methods : This was a nonreactive study, which is a measurement which individuals surveyed did not realize that they are part of a study. Result : Based on the best model selection criteria MARS then the selected is with model BF 20, MI 1 and MO 0 with the form Y = 0.929944 + 0.912438 * BF1 - 0.218729 * BF2 + 0.886429 * BF3 + 0.215575 * BF4 + 0.0745423 * BF5 - 0.232014 * BF6 + 0.0472966 * BF7 - 0.0367996 * BF8 + 0.0188678 * BF9 + 0.0304537 * BF11. Accuracy of drugs user rehabilitation classification that non relapse and relapse status based on MARS model is calculated using precision classification value. The accuracy level of drugs user rehabilitation classification in East Java using MARS method produces accuracy of  95,71% and misclassification of 4,29%. The magnitude of the above classification accuracy is due to the large prediction in the nonrelapse class that as many as 269 people with nonrelapse status are appropriately predicted in the nonrelapse status class. Conclusion : There are four important variables included in the best MARS model that is age of first use of drugs, how to use drugs, marital status and jobs. The accuracy level of drugs user rehabilitation classification in East Java using MARS method  produces accuracy of 95,71% and misclassification of 4,29%.    
Co-Authors Arwati, Niluh Dwi Winarni Kuntoro Kuntoro Merryana Adriani Sri Hartiningsih Stefanny Zulistya Wenno, Stefanny Zulistya Wijatmadi, Bambang