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All Journal Jurnal Keperawatan Priority Obat: Jurnal Riset Ilmu Farmasi dan Kesehatan
Rumiris Simatupang
Sekolah Tinggi Ilmu Kesehatan Nauli Husada
Dentistry Health Professions Medicine & Pharmacology Nursing Public Health

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Machine Learning Models Prediction Medication Nonadherence Risk in Type 2 Diabetes: A Systematic Review Victor Trismanjaya Hulu; Yusuf Panserito Hulu; Kharis Meiwan K Telaumbanua; Reni Aprinawaty Sirait; Arianus Zebua; Rumiris Simatupang
Jurnal Keperawatan Priority Vol. 9 No. 1 (2026)
Publisher : Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jukep.v9i1.7859

Abstract

The prediction of medication nonadherence among patients with T2DM can be improved in accuracy and speed using machine learning (ML). This study aimed to develop an ML model to predict the risk of medication nonadherence among patients with T2DM. Methods, inclusion criteria comprised English-language, open-access journal articles published between 2020 and 2025 that developed and validated ML–based prediction models, including ensemble methods, gradient-boosting models, SVMs, and neural networks. Exclusion criteria included review articles, non-English papers, studies published before 2020, studies lacking prediction model development or validation, and studies using only traditional statistical methods, such as logistic regression. The article search was conducted in PubMed, Scopus, ScienceDirect, and Google Scholar. Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess the methodological quality and usefulness of the qualified studies. This narrative synthesis examines the characteristics of ML-based prediction models, their performance, and the factors that predict adherence among patients with T2DM. The papers were sourced from various scientific journal databases. The results show that cross-sectional and cohort studies were among the research designs used in the five papers reviewed. The AUROC of the internal test was 0.782, and the AUROC of the external test was 0.771. The learned-feature classification model achieved an average accuracy of 79.7%. Among these algorithms, the AUC of the best-performing algorithm was 0.866 ± 0.082. The SVM classifier outperformed the others, achieving a recall of 0.9979 and an AUC of 0.9998. The conclusion indicates that predictive capacity is influenced by clinical metrics and the number of prescribed medications.
Efektivitas Dapur Umum di Posko Tanggap Darurat Bencana (TDB) dalam Pemenuhan Kebutuhan Gizi pada Anak-Anak Pasca Bencana di Kelurahan Hutanabolon Kecamatan Tukka Fridella Grace Natalia Tarigan; Rumiris Simatupang; Percaya Hia; Siti Ratna Harefa; Soeandi Malik Pratama
OBAT: Jurnal Riset Ilmu Farmasi dan Kesehatan Vol. 4 No. 1 (2026): Januari: OBAT: Jurnal Riset Ilmu Farmasi dan Kesehatan
Publisher : Asosiasi Riset Ilmu Kesehatan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/obat.v4i1.2118

Abstract

Floods and landslides in Hutanabolon Village, Tukka District, Central Tapanuli Regency have disrupted access to food and health services, particularly for vulnerable groups such as children. Public kitchens, as the frontline of emergency nutrition response, face challenges in providing food that meets the specific nutritional needs of children. This study aims to evaluate the effectiveness of public kitchens at Disaster Emergency Response Posts (TDB) in meeting the nutritional needs of post-disaster children, identify supporting and inhibiting factors, and formulate recommendations for improving the public kitchen management system. The research employed a descriptive evaluative approach using survey methods, structured interviews, direct observation, 24-hour dietary recall, and anthropometric measurements (weight and height). The study subjects included 15 children aged 1–12 years and 8 public kitchen managers selected purposively. Data were analyzed descriptively by comparing nutritional intake against the Recommended Dietary Allowance (RDA) standards and analyzing kitchen management practices based on emergency nutrition guidelines. The findings revealed that children's average energy intake was only 1,140 kcal/day (below the minimum standard of 1,200–2,000 kcal/day) and protein intake was 18.7 g/day (below the standard of 20–35 g/day). A total of 33.3% of children were classified as having malnutrition to severe malnutrition based on weight-for-age indicators. Public kitchen management showed weaknesses in menu planning (100% had no child-specific menu), managers' nutritional knowledge (62.5% categorized as low), food availability (75% relied on instant aid without variation), and limited cross-sectoral coordination (50%). The effectiveness of public kitchens in meeting children's nutritional needs after disasters remains low.
Co-Authors Arianus Zebua Fridella Grace Natalia Tarigan Kharis Meiwan K Telaumbanua Percaya Hia Reni Aprinawaty Sirait Siti Ratna Harefa Soeandi Malik Pratama Victor Trismanjaya Hulu Yusuf Panserito Hulu