Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Ners J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan
Rury Moryanda
Universitas Syedza Saintika
Computer Science & IT Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
SMART TRACER BERKAS REKAM MEDIS: TRACKING LOKASI DAN STATUS BERKAS, PEMINJAMAN, PENGEMBALIAN, SERTA NOTIFIKASI KETERLAMBATAN OTOMATIS Denos Imam Fratama; Dede Fauzi; Rury Moryanda
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 5 (2026): October (2026)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i5.7248

Abstract

Abstract: Manual management of medical record files in healthcare facilities frequently encounters obstacles such as high missfile rates, prolonged retrieval times, and excessive overdue returns exceeding the 24-hour standard limit. Conventional paper-based tracers have proven ineffective due to physical wear, misplacement, and the absence of real-time tracking mechanisms. This study aims to design and develop a web-based Smart Tracer system integrating precise shelf and sub-box location tracking, digitized borrowing and returning transactions via barcode/QR code scanning, and automated overdue notification delivery using a WhatsApp API Gateway. The system was developed using a Research and Development (R&D) methodology following the Waterfall model, comprising requirements analysis, UML and ERD system design, implementation using PHP Laravel framework and MySQL database, and rigorous testing. Functional testing using Black Box Testing revealed that all 12 test scenarios were 100% valid. User Acceptance Testing (UAT) using a 5-point Likert scale across 25 respondents achieved an overall acceptance rate of 93.4% (Very Good category). Implementation of the Smart Tracer successfully reduced average file retrieval time from 18.0 minutes to 3.0 minutes (83.3% efficiency gain) and significantly suppressed overdue file returns from an average of 55 cases per month to 4 cases per month (91.8% reduction). This system offers an applicable digital solution that ensures rapid, accurate, and accountable medical record availability. Keywords: medical record, smart tracer, information system, file tracking, overdue notification   Abstrak: Pengelolaan berkas rekam medis manual di fasilitas pelayanan kesehatan sering menghadapi kendala tingginya angka berkas salah letak (missfile), waktu pencarian yang lama, serta tingginya keterlambatan pengembalian berkas yang melebihi standar waktu 1x24 jam. Penggunaan tracer fisik berbahan kertas konvensional terbukti tidak efektif karena mudah rusak, hilang, dan tidak menyediakan mekanisme pelacakan posisi berkas secara real-time. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem Smart Tracer berbasis web yang mengintegrasikan pelacakan lokasi rak dan sub-box presisi, digitalisasi pencatatan peminjaman dan pengembalian menggunakan pemindaian kode batang (barcode/QR), serta automasi pengiriman notifikasi keterlambatan melalui WhatsApp API Gateway. Metode pengembangan sistem yang digunakan adalah Research and Development (R&D) dengan model Waterfall yang mencakup tahap analisis kebutuhan, perancangan sistem dengan diagram UML dan ERD, implementasi menggunakan framework PHP Laravel dan database MySQL, serta pengujian sistem. Pengujian fungsionalitas menggunakan Black Box Testing menunjukkan seluruh 12 skenario berjalan 100% valid. Evaluasi penerimaan pengguna melalui User Acceptance Testing (UAT) dengan instrumen skala Likert terhadap 25 responden menghasilkan tingkat penerimaan keseluruhan sebesar 93.4% (kategori Sangat Baik). Implementasi Smart Tracer terbukti memangkas rata-rata waktu pencarian berkas dari 18.0 menit menjadi 3.0 menit (efisiensi 83.3%) serta menurunkan kasus keterlambatan pengembalian berkas secara signifikan dari rata-rata 55 kasus per bulan menjadi 4 kasus per bulan (penurunan 91.8%). Sistem ini memberikan solusi digital aplikatif yang menjamin ketersediaan berkas rekam medis secara cepat, akurat, dan akuntabel. Kata Kunci: rekam medis, smart tracer, sistem informasi, tracking berkas, notifikasi keterlambatan
Co-Authors Dede Fauzi Denos Imam Fratama Dicky Fatrias Ika Yulia Darma Meldafia Idaman Nurul Abdillah Silfina Indriani Silvi Zaimy Wahna Khaula