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All Journal Jurnal Ners J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan
Rury Moryanda
Universitas Syedza Saintika
Health Professions Medicine & Pharmacology Nursing Public Health

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Hubungan Praktik Self Care dengan Kesejahteraan Psikologis Ibu Nifas di Wilayah Kerja Puskesmas Lubuk Buaya Kota Padang Ika Yulia Darma; Wahna Khaula; Silvi Zaimy; Meldafia Idaman; Rury Moryanda; Silfina Indriani
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.v9i3.45753

Abstract

The postpartum period is a critical recovery phase for mothers that involves significant physiological and psychological changes. Inadequate adaptation during this period can affect the psychological well-being of mothers, increasing the risk of postpartum depression, baby blues, or even psychosis. One important factor contributing to maternal psychological well-being is self-care practice, which refers to the mother’s ability to care for herself independently. This study aimed to examine the relationship between self-care practices and the psychological well-being of postpartum mothers in Pasia Nan Tigo Village, within the Lubuk Buaya Public Health Center working area, Padang City. This research employed a quantitative method with a cross-sectional approach and total sampling technique. A total of 32 postpartum mothers were included in the study. Data were collected using structured questionnaires and analyzed using the Chi-Square test. The findings revealed that 85.7% of respondents had poor self-care practices, and most of them were at risk of psychological distress. Statistical analysis confirmed a significant relationship between self-care practices and psychological well-being (p < 0.05). This study highlights the importance of educational support and promotion of self-care practices to enhance maternal psychological health in the postpartum period.
Pendekatan Machine Learning untuk Menganalisis Faktor-Faktor yang Berkontribusi Terhadap Kesalahan Pengkodean ICD-10 di Semen Padang Hospital Rury Moryanda; Nurul Abdillah; Denos Imam Fratama; Dicky Fatrias
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6775

Abstract

ICD-10 coding errors remain a major challenge in healthcare facilities, affecting the validity of morbidity data, the accuracy of BPJS Kesehatan claims processing, and the quality of health information management. This study aimed to identify the main factors contributing to ICD-10 coding errors and evaluate the effectiveness of machine learning algorithms in detecting such errors. Electronic medical record data were analyzed through data cleaning, preprocessing, descriptive analysis, and machine learning modeling. Three algorithms were applied: Random Forest, Support Vector Machine (SVM), and Neural Network. Model performance was evaluated using accuracy, precision, and sensitivity metrics. The findings revealed an ICD-10 coding error rate of 33.8%, primarily caused by nonspecific diagnoses and insufficient clinical information. Among the tested models, the Neural Network achieved the highest accuracy (72%), followed by SVM (68%) and Random Forest (60%). These results suggest that machine learning techniques can effectively support the early detection of ICD-10 coding errors and enhance the quality of health data management. The adoption of machine learning–based predictive models may improve coding accuracy and facilitate evidence-based decision-making in health information management.
Co-Authors Denos Imam Fratama Dicky Fatrias Ika Yulia Darma Meldafia Idaman Nurul Abdillah Silfina Indriani Silvi Zaimy Wahna Khaula