Juang Bertorio, Margala
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HANDGRIP STRENGTH AND DASH EATING BEHAVIOR IS RELATED TO HIGHER BLOOD PRESSURE ON PRE-ELDERLY AND ELDERLY IN YOGYAKARTA Fayasari, Adhila; Febri Nilansari, Anis; Juang Bertorio, Margala
Media Gizi Indonesia Vol. 20 No. 2 (2025): MEDIA GIZI INDONESIA (NATIONAL NUTRITION JOURNAL)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/mgi.v20i2.135-143

Abstract

Handgrip strength (HGS) reflects muscle health and is linked to hypertension, yet its relationship with blood pressure (BP) in older adults is underexplored. DASH dietary patterns, crucial for BP management, may provide further insights into this connection. This study aims to examine the association between HGS and DASH eating behavior with blood pressure in pre-elderly and elderly populations. A cross-sectional study was conducted on pre-elderly and elderly outpatient at Wirosaban Hospital, Yogyakarta in July to August 2024. HGS was measured using a hand dynamometer, while BP was recorded using a standard sphygmomanometer. Adherence to DASH were measured by 24-hour food recall and then categorized by questionnaire of DASH eating behavior. Data were analyzed by correlation and multiple regression, with α 5%. Hypertension was found in about 68.8% of subjects. Subjects who have lower HGS were about 68.8% and 81.7% of low category of DASH eating behavior. Participants with lower HGS had notably higher systolic and diastolic BP compared to those with higher HGS. Stratification analysis revealed that there were no significant association between HGS and high blood pressure both in elderly and pre-elderly (p 0.063 and p 1.000). In the other hand, low DASH eating behavior was significantly related to higher blood pressure in pre-elderly group (p 0.031). Reduced HGS is likely linked to higher BP in the elderly. Maintaining DASH-like diet adherence and muscle strength may be crucial in mitigating hypertension risk and comorbidity in older adults.
Advanced drug recommendation using long short-term memory and type-2 fuzzy logic integration Fairuzabadi, Muhammad; Rianto, Rianto; Juang Bertorio, Margala
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9180

Abstract

This research on hybrid models for drug recommendation systems proposes long short-term memory (LSTM) and type-2 fuzzy logic (T2FL) to make its recommendations more accurate and reliable. The model leverages LSTM's ability to capture temporal patterns in medical data while addressing the inherent uncertainty through T2FL. Evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), accuracy, precision, recall, F1-Score, and area under the curve-receiver operating characteristic (AUC-ROC) demonstrate that the proposed model significantly outperforms traditional models like LSTM without fuzzy, linear regression, and random forest. Integrating these two methods results in more accurate and consistent predictions, making the model highly effective in handling complex and uncertain data. Practical implications include the potential for improving personalized treatment plans and patient outcomes in clinical settings. Future research directions involve applying this hybrid approach to larger, more diverse datasets and exploring additional hybrid methods that enhance prediction accuracy and model robustness. The findings suggest that the LSTM+T2FL model is a promising tool for advancing drug recommendation systems in the medical field.