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Pola Makan Dan Status Gizi Perokok Di Sulawesi Tengah: Analisis Data Riskesdas 2018: Pola Makan dan Status Gizi Perokok di Sulawesi Tengah: Analisis Data Riset Kesehatan Dasar 2018 Kurniasari, Dian; Galenso, Nitro; Hafid, Fahmi
Amerta Nutrition Vol. 8 No. 1 (2024): AMERTA NUTRITION (Bilingual Edition)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/amnt.v8i1.2024.8-16

Abstract

Background: According to the National Basic Health Research of Indonesia (Riskesdas) in 2018, the prevalence of non-communicable diseases (NCDs) in Central Sulawesi Province surpassed the national rate. This province is also home to 31.3% of active smokers. The main risk factor for NCDs is an unhealthy lifestyle such as physical inactivity, smoking, alcohol consumption, and unhealthy diet pattern. Objectives: This study aims to investigate diet patterns and nutritional status of adults in Central Sulawesi based on their smoking status. Methods: This is a cross-sectional study using data from Riskesdas 2018. Smoking status, diet pattern and nutritional status were the variables assessed from 12,211 respondents. The statistical analyses used in this study were chi-square test and Generalized Linear Model (GLM). Results: Thirty-six-point two per cent of the respondents were active smokers. The prevalence of overweight (BMI ≥25 kg/m2) and central obesity in smokers were significantly lower than that in the non-smoker group (p<0.05). The smoker group consumed sweetened drinks at least once a week, which increased the prevalence ratio as the weekly consumption frequency increased (PR=1.17 to 1.49; p<0.05). They also had a lower prevalence ratio of consumption a maximum of 3-4 portions/day of vegetable and consuming salty food (high sodium) 1-2 times a week. Conclusions: Although the prevalence ratio of overweight and central obesity are lower in smokers, they have unhealthy diet patterns, which are shown in lower consumption of vegetables and high in salty food. In the future, an advanced longitudinal study with representative number of respondents is needed to explore the causality of these variables.
Understanding Consumer Sentiments: A TextBlob-Based Sentiment Analysis Study Kurniasari, Dian; Hdiana, Yazid Zinedine; Lumbanraja, Favorisen R.; Warsono, Warsono; Hadi, Normi Abdul
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 3 (2025): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252340

Abstract

This study employs advanced sentiment analysis techniques to enhance the understanding of drug reviews, with a specific focus on TextBlob-based sentiment classification. As the accessibility of health products through pharmacies and online platforms continues to increase, individuals with limited health literacy are increasingly relying on user-generated feedback to inform their decision-making. By utilizing the TextBlob labelling method, this research categorizes user sentiments into positive, neutral, or negative, addressing the limitations inherent in traditional sentiment analysis approaches. The analysis is supported by an innovative model known as BERT, which effectively captures the emotional expression within textual data. The results indicate that the proposed approach consistently achieves an accuracy of 98% across training, validation, and testing phases, highlighting its strong performance in sentiment classification. This accomplishment underscores TextBlob’s ability to consistently and reliably assess user sentiment, thereby enriching the understanding of consumer perspectives in the pharmaceutical industry. The findings highlight the importance of effective sentiment analysis methods in healthcare, offering valuable insights for both consumers and stakeholders. Moreover, this study provides a foundation for future investigations focused on improving sentiment analysis methods across varied datasets, which will enhance the precision and applicability of classification results in different scenarios.