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Arianus Zebua
Sekolah Tinggi Ilmu Kesehatan Arta Kabanjahe
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Studi Cross-Sectional Kualitas Pelayanan Administrasi Terhadap Kepuasan Pasien Arianus Zebua; Herianto Bangun
Jurnal Ners Vol. 9 No. 3 (2025): JULI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i3.47299

Abstract

Kualitas pelayanan administrasi menjadi elemen penting yang memengaruhi tingkat kepuasan pasien, khususnya dalam konteks persaingan layanan kesehatan yang semakin intensif. Pelayanan yang berkualitas tidak hanya merefleksikan performa lembaga kesehatan, tetapi juga turut membentuk persepsi serta pengalaman pasien terhadap pelayanan yang diberikan. Penelitian ini bertujuan untuk mengetahui hubungan kualitas pelayanan administrasi terhadap kepuasan pasien. Desain penelitian yang digunakan adalah cross-sectional. Penelitian ini dilakukan di Klinik Permata Saribudolok Tahun 2023. Subjek penelitian ini adalah seluruh pasien yang berkunjung di Klinik Permata Saribudolok Tahun 2023. Sampel diperoleh sebanyak 56 orang dengan teknik total sampling. Pengumpulan data dilakukan dengan menyebarkan kuesioner kepada pasien. Analisis data penelitian menggunakan uji Chi Square. Sebanyak 60,7% pasien merasa puas terhadap pelayanan administrasi yang diberikan tenaga kesehatan. Dimensi penampilan (p = 0,003; PR = 6,75; 95%CI 2,028-22,463), kehandalan (p = 0,031; PR= 4,01; 95%CI 1,281-12,563), daya tanggap (p = 0,010; PR = 5,14; 95%CI 1,610-16,430), perhatian (p = 0,011; PR = 4,83; 95%CI 1,362-17,157), dimensi jaminan (p = 0,011; PR = 6,96; 95%CI 2,104-23,053) berhubungan signifikan terhadap kepuasan pasien.
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.
Co-Authors Herianto Bangun Kharis Meiwan K Telaumbanua Reni Aprinawaty Sirait Rumiris Simatupang Victor Trismanjaya Hulu Yusuf Panserito Hulu