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All Journal Jurnal Kebijakan Kesehatan Indonesia Ekonomikawan : Jurnal Ilmu Ekonomi dan Studi Pembangunan Public Health of Indonesia Paediatrica Indonesiana PEDAGOGIA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Kesehatan Medika Saintika Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Jurnal Pengabdian Masyarakat AbdiMas Jurnal Kreativitas PKM JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Manajemen Informatika Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Indonesian of Health Information Management Journal (INOHIM) Poltekita : Jurnal Ilmu Kesehatan PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Ners Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan KESANS : International Journal of Health and Science NUSRA: Jurnal Penelitian dan Ilmu Pendidikan Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Sehat Rakyat: Jurnal Kesehatan Masyarakat PELS (Procedia of Engineering and Life Science) Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) Journal Health Information Management Indonesian Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics Cerdika: Jurnal Ilmiah Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) Eduvest - Journal of Universal Studies Jurnal Penelitian Sistem Informasi International Journal of Health and Information System (IJHIS) Jurnal Bioteknologi & Biosains Indonesia (JBBI) Jurnal Bioteknologi & Biosains Indonesia Jurnal Ilmu Kesehatan Masyarakat Jurnal Karya untuk Masyarakat (JKuM)
Nurmalasari, Mieke
Universitas Esa Unggul
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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THE RELATIONSHIP OF TNFα -308 G/A POLYMORPHISM WITH THE INCIDENCE OF CERVICAL CANCER IN ASIAN WOMEN: A META ANALYSIS OBSERVATIONAL STUDY Saraswati, Henny; Nurmalasari, Mieke
Jurnal Bioteknologi & Biosains Indonesia (JBBI) Vol. 11 No. 1 (2024)
Publisher : BRIN - Badan Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jbbi.2024.2546

Abstract

Cervical cancer is a malignancy with high mortality rates in women, and its incidence continues to rise. The main etiological factor for cervical cancer is infection with Human Papillomavirus (HPV), which disrupts the regulation of apoptosis in cells. Several studies have shown a correlation between TNFα polymorphisms, including the -308 position (TNFα -308 G/A), and the incidence of cervical cancer.This gene have a role in proliferation of cancer cells. This study investigates the impact of TNFα-308 polymorphism on the risk of cervical cancer in Asian female populations. A meta-analysis of five sources was conducted to determine potential associations. Findings reveal that neither allele A (OR 95%CI = 1.20 [0.70-2.03], p = 0.51) nor genotype AA (OR 95%CI = 0.85 [0.37-1.91], p = 0.69) were significantly linked with an elevated risk of cervical cancer in Asian women. The same result was seen for the G allele (OR 95%CI = 0.84 [0.49-1.42], p = 0.51) and GG genotype (OR 95%CI = 0.80 [0.44-1.48], p = 0.48). The study results indicate that the TNFα-308 polymorphism is not associated with cervical cancer in Asian women. Further research is needed to investigate the role of other gene polymorphisms in cervical cancer susceptibility in Asian women.
Forecasting Kapasitas Tempat Tidur di Rumah Sakit Islam Jakarta Pondok Kopi Sinlae, Andrey Reynaldi Devada; Hosizah, Hosizah; Nurmalasari, Mieke
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 2 (2026): March
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i2.5727

Abstract

In 2022–2023, bed utilization at RSIJ Pondok Kopi was inefficient according to the Barber–Johnson (GBJ) standard, with Bed Occupancy Rates (BOR) of 53% in 2022 and 74% in 2023, both below the ideal range. This inefficiency was partly due to the absence of bed capacity adjustments based on accurate forecasting. This study aimed to conduct bed capacity forecasting at RSIJ Pondok Kopi. This applied retrospective study employed data mining techniques using Tableau with the Exponential Smoothing algorithm. Data on inpatient days and discharged patients from 1992 to 2023 were collected and processed following the Knowledge Discovery in Databases (KDD) framework. One optimal forecasting model was selected for each variable. Bed capacity projections were calculated using BOR assumptions of 75% and 85%, and Turnover Interval (TOI) assumptions of 1 and 3 days, with the Barber–Johnson chart used for evaluation. Forecasted bed requirements were estimated at 183–192 units (2024), 185–194 (2025), 187–196 (2026), 189–197 (2027), and 190–199 (2028). Compared with actual data through May 2024, the hospital had 10 excess beds. Therefore, more intensive promotional strategies are recommended to improve bed utilization.
Penyuluhan Sistem Informasi Posyandu sebagai Upaya Mewujudkan Bebas Stunting Arief Ichwani; Mieke Nurmalasari; Nizirwan Anwar; Widia Sari; Badie Uddin
Jurnal Karya untuk Masyarakat (JKuM) Vol 5 No 1: JANUARI 2024
Publisher : Universitas Tarakanita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36914/wpbnxg50

Abstract

Desa Pasirwaru merupakan desa dengan jumlah penduduk 5.511 jiwa dan memiliki program unggulan di bidang kesehatan yaitu posyandu. Posyandu Anggrek I adalah posyandu di Desa Pasirwaru yang memiliki anggota 60 balita, 10 ibu hamil. Adapun peranan dari posyandu ini yaitu untuk memberdayakan, memberikan kemudahaan layanan kesehatan, penyuluhan untuk mengatasi stunting dengan pemenuhan kebutuhan gizi bagi ibu hamil, memberikan ASI dan MPASI, akses air bersih, dan memantau pertumbuhan balita di posyandu. Kegiatan pemantauan pertumbuhan balita, dan ibu hamil di posyandu harus didukung oleh data yang lengkap dan akurat dari hasil setiap kegiatan posyandu. Adapun laporan data balita, ibu hamil saat ini di Posyandu Anggrek I masih menggunakan buku tulis. Hal tersebut menyebabkan beberapa permasalahan seperti buku mudah rusak, hilang, ketidak sesuaian dan ketidak akuratan pelaporan, duplikasi data , tidak konsisten, hak akses data yang tidak terkondisikan, media penyimpanan bersifat sementara, sulit dilakukan pengolahan data untuk menghasilkan informasi penting tentang gambaran kondisi balita dan ibu hamil di lingkungan posyandu. Oleh karena itu posyandu harus memiliki Sistem Informasi Posyandu untuk pelaporan data, pencarian data, pemantauan pertumbuhan balita dan ibu hamil yang diakses dengan mudah dan cepat sehingga memiliki keakuratan, keamanan, ketersediaan, kelengkapan data berkelanjutan dan mendukung pengambilan keputusan dengan efektif dan efesien. Metode yang digunakan pada pengabdian masyarakat ini yaitu penyuluhan kesehatan dan workshop penggunaan Sistem Informasi Posyandu. Adapun hasil dari kegiatan ini adalah peningkatan kesadarana akan pentingnya kesehatan, tersedianya sistem informasi posyandu yang dapat digunakan masyarakat dan kader posyandu dengan baik dan benar untuk pemantauan tumbuh kembang anak dan ibu hamil.
Evaluation of User Satisfaction in the Satusehat Application Sulistianingsih Sulistianingsih; Mieke Nurmalasari; Hosizah Hosizah; Witri Zuama Qomarania
Jurnal Ilmu Kesehatan Masyarakat Vol. 15 No. 3 (2024): Jurnal Ilmu Kesehatan Masyarakat (JIKM)
Publisher : Association of Public Health Scholars based in Faculty of Public Health, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26553/jikm.2024.15.3.346-359

Abstract

Digital transformation in the health sector aims to provide quality, fast, easy, affordable, and measurable services to the community. User satisfaction is paramount when providing services. Key factors influencing user satisfaction include Information, System, and Service Quality. The objective of this study is to evaluate the impact of information quality, system quality, and service quality on user satisfaction with the SATUSEHAT. This research used a quantitative with a cross-sectional design technique. This research using a quota sampling method with 106 respondents. This research using multiple linear regression analysis with univariate and multivariate classical assumption tests. This research focuses on SATUSEHAT application users who actively use social media platforms like Instagram and Twitter. The evaluation aims to provide insights into the impact of information, system, and service quality of the SATUSEHAT application's user satisfaction with the SATUSEHAT application. The results explain that 56.6% of respondents were female, 43.3% were male. 54.7% of this study's respondents were undergraduates aged 19 - 34 years, 67.0% of respondents. Most of the respondents' jobs were employees, 34.9%. Service quality significantly influences SATUSEHAT application The results of the regression coefficient value for user satisfaction is 0.651, and quality of the information, with a regression coefficient of 0.113. The The study found that information, system, and service quality significantly influence user satisfaction with the SATUSEHAT application.
RELATIONSHIP RELATIONSHIP BETWEEN COMPLETENESS OF DIAGNOSIS WRITING AND THE ACCURACY OF INJURY CASE CODEFICATION OF INPATIENTS AT RAA SOEWONDO PATI REGIONAL HOSPITAL: HUBUNGAN KELENGKAPAN PENULISAN DIAGNOSIS DENGAN KETEPATAN KODEFIKASI KASUS CEDERA PASIEN RAWAT INAP DI RSUD RAA SOEWONDO PATI Kafida Mey Dhani Rahmat; Ambarwati; Hosizah Markam; Mieke Nurmalasari
Journal Health Information Management Indonesian Vol. 4 No. 3 (2025): Desember (Journal Health Information Management Indonesian)
Publisher : Sekretariat Program Studi Sarjana Terapan Manajemen Informasi Kesehatan Politeknik Indonusa Surakarta.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/mik.v4i3.200

Abstract

The completeness of the medical record is very important, especially on the medical resume sheet which contains a summary of the patient's diagnosis. The completeness of the medical record greatly affects the accuracy of the disease codification. This study aims to determine the relationship between the completeness of writing a diagnosis and the accuracy of the coding of cases of injury to inpatients. This research is an analytical survey research with a quantitative approach. The study population was 94 medical records so that a sample of 49 medical records was obtained using a systematic random sampling technique. Data analysis of this study used the chi square test with a cross-sectional research design. The percentage of completeness in writing the diagnosis was 46.9% complete while 53.1% was incomplete. The accuracy of the codeification of injury cases is 36.7% correct and 63.3% is incorrect, while the percentage of external cause coding is 0% because there is no external cause coding by the officer. Fishers exact test showed that the value of p = 0.000 0.05. This means that H1 is accepted and Ho is rejected so that there is a relationship between the completeness of writing a diagnosis and the accuracy of the codefication of inpatient cases of injury at the RAA Soewondo Pati Hospital. The author suggests that there is communication between the medical record officer and doctors and nurses in order to write down the complete diagnosis and supporting information about the external cause in the case of injury, the coder is obliged to provide an external cause code in the case of injury and revision of the SOP regarding the provision of a diagnosis code
Hubungan Literasi Digital dengan Kesiapan Teknologi menggunakan TRI (Technology Readiness Index) pada Pengguna Rekam Medis Elektronik di Klinik Utama Grha Atma Bandung Yosi Siti Nur Azizah; Tria Saras Pertiwi; Hosizah Hosizah; Mieke Nurmalasari
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.4020

Abstract

Digital transformation in healthcare encourages healthcare facilities to implement Electronic Medical Records (EMR). The success of EMR implementation is not only determined by system and infrastructure readiness but also influenced by users' digital literacy and technology readiness. This study aimed to analyze the relationship between digital literacy and technology readiness using the Technology Readiness Index (TRI) among EMR users at Grha Atma Primary Clinic Bandung. This study used a quantitative approach with a cross-sectional design. The sample consisted of 31 respondents using total sampling. Data were collected using a digital literacy questionnaire adapted from DHLI and a TRI questionnaire. Data analysis included univariate analysis (Three Box Method) and bivariate analysis using Spearman Rank correlation. The results showed that digital literacy was categorized as high across all dimensions. Technology readiness indicated that optimism and innovativeness were high, while discomfort and insecurity ranged from moderate. Spearman test results showed a significant positive relationship between digital literacy and technology readiness (p = 0.003; r = 0.519). This study concludes that digital literacy significantly influences technology readiness among EMR users. Improving digital literacy is essential to enhance technology readiness and optimize EMR implementation.
PERAN DUKUNGAN KELUARGA DALAM MEMODERASI PENGARUH KUALITAS PELAYANAN TERHADAP MINAT KUNJUNGAN ULANG Indah Cahyani Buana; Mieke Nurmalasari; Hosizah Markam; Witri Zuama Qomarania
Jurnal Kesehatan Medika Saintika Vol 17, No 1 (2026): Juni 2026
Publisher : Stikes Syedza Saintika Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30633/jkms.v17i1.30780

Abstract

Penelitian ini dilatarbelakangi oleh ketidakstabilan jumlah kunjungan ulang di Rumah Sakit Soeharto Heerdjan pada tahun 2023 dan 2024, serta keluhan terkait waktu tunggu dan sikap petugas pada Google Review Rumah Sakit Soeharto Heerdjan periode November-Desember 2024. Penelitian ini bertujuan untuk mengkaji pengaruh kualitas pelayanan terhadap minat kunjungan ulang pasien di Rumah Sakit Soeharto Heerdjan serta menguji peran dukungan keluarga dalam memoderasi pengaruh tersebut. Penelitian menggunakan pendekatan kuantitatif dengan desain cross-sectional, dilaksanakan pada bulan Mei-Agustus 2025 pada 119 responden yang dipilih secara accidental sampling menggunakan kuesioner. Analisis data menggunakan analisis univariat dan multivariat. Hasil analisis menunjukkan nilai rata-rata variabel minat kunjungan ulang sebesar 13,34 (SD=1,464), kualitas pelayanan 119,37 (SD=5,563), dan dukungan keluarga 50,13 (SD=3,539). Kualitas pelayanan berpengaruh positif terhadap minat kunjungan ulang (p-value=0,001; β=0,840; adjusted R²=70,2%), dan dukungan keluarga mampu memoderasi pengaruh kualitas pelayanan terhadap minat kunjungan ulang (p-value=0,037; β=4,084; adjusted R²=80,7%). Disarankan rumah sakit mempertahankan dan meningkatkan kualitas pelayanan pada semua dimensi SERVQUAL, serta memperkuat minat kunjungan ulang dengan mengoptimalkan peran dukungan keluarga pasien. 
ICD Coding Automation Model of Retinal Detachment Case Using Support Vector Machine and Random Forest Dyah Kurniawati; Mieke Nurmalasari; Hosizah Markam; Dewi Krismawati
Eduvest - Journal of Universal Studies Vol. 6 No. 5 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i5.53072

Abstract

Health Information Management (HIM) professionals are responsible for maintaining the consistency of ICD-based clinical codes for the health reimbursement and health analytics through the review of medical documentation. The complexity of coding rules and clinical pathways increases the risk of miscoding, but the implementation of Electronic Medical Record (EMR) opens opportunities for the development of automation of ICD coding. This study aims to build an ICD code automation model for retinal detachment cases from eye referral hospital using artificial intelligence through clinical text classification with Natural Language Processing (NLP) and Machine Learning (ML) algorithms. The dataset includes disease resumes, physical examinations, diagnoses, medical procedures, surgical records, and therapies from 300 inpatients. Text preprocessing uses the NLTK library through sentence splitting, abbreviation expansion, case folding, stop word removal, and tokenization functions. Data preparation involves splitting data (80:20 ratio), feature extraction with TF-IDF Vectorizer, and 5-fold cross validation. Classification modeling uses Support Vector Machine (SVM) and Random Forest (RF). Evaluation of the SVM model showed an accuracy of 0.82 (precision 0.84; recall 0.82; F1-Score 0.82), while the RF model achieved an accuracy of 0.87 (precision 0.88; recall 0.87; F1-Score 0.87). Based on confusion metrics, the correct predictions for classes H33.0, H33.2, and H33.4 on SVM are 79, 87, and 80, while RF reaches 83, 88, and 91. The development of this automation requires HIM professional’s role in ensuring the quality of EMR data and accuracy of ICD code as well as intensive model training to handle the complexity of clinical data.
Convolutional Neural Networks-Based Deep Learning for Diabetic Retinopathy Detection Mieke Nurmalasari; Anastasia Cyntia Dewi Kurniawati; Agus Herwanto; Dyah Kurniawati; Husni Abdul Muchlis; Tria Saras Pertiwi
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetic retinopathy (DR) is a major complication of diabetes that can cause permanent vision loss, affecting about 35% of people with type 2 diabetes worldwide. However, existing diagnostic models often struggle with class imbalance and limited generalizability across diverse real-world datasets. Early detection is crucial, yet manual screening is time-consuming and depends on expert assessment. This study develops an automated DR diagnostic system using deep learning to classify fundus images by severity. The model uses an EfficientNetB3 CNN pretrained on ImageNet, combined with CLAHE preprocessing to enhance image contrast. The preprocessing steps include resizing, CLAHE, normalization, and data augmentation (±20° rotation, horizontal flipping, and ZCA whitening). The dataset is the Gaussian-filtered APTOS 2019 set, consisting of 2,750 images across five DR levels (0–4). The model achieved 95% training accuracy and 75% validation accuracy, with overfitting observed after epoch 14. While training performance was high, evaluation metrics (Precision, Recall, F1-Score, and AUC) indicate the need for early stopping or regularization to improve generalization. Overall, CNN-based deep learning can effectively automate DR detection, though further optimization is required for better performance on unseen data. Clinically, this automated pipeline offers a reliable decision-support tool to prioritize high-risk patients for immediate ophthalmological review
Perbandingan Decision Tree dan Neural Network dalam Prediksi LVEF pada Pasien Gagal Jantung Eva Rahmawati; Mieke Nurmalasari; Hosizah Markam; Dhiar Niken Larasati
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2438

Abstract

Heart failure is a cardiovascular disease with a high mortality rate, affecting more than 64 million people worldwide, with a one-year fatality rate of 33%. To assess cardiac performance, the Left Ventricular Ejection Fraction (LVEF) indicator was used, which reflects the ability of the left ventricle to pump blood. Therefore, an analytical approach is needed to predict LVEF values more accurately based on patient characteristics. This study aimed to compare the performance of Decision Tree and Neural Network algorithms in predicting LVEF values in patients with heart failure. Data processing was conducted using Orange Data Mining, utilizing risk factor variables as the basis for classification. The approach used was supervised learning, involving 381 heart failure patient records that were split using an 80:20 train-test split technique, resulting in 305 training data and 76 testing data. Class imbalance in the training data was handled using the SMOTE method before the modeling process. The experimental results show that the model built using the Neural Network algorithm provides better performance than the Decision Tree. This was indicated by an AUC value of 64.8%, classification accuracy of 57.9%, F1-score of 55.8%, precision of 57.6%, and recall of 57.9%. In addition, based on the confusion matrix evaluation, the Neural Network algorithm was able to achieve higher accuracy and recall levels in most LVEF categories. Based on these results, it can be concluded that the Neural Network is a more effective method for predicting LVEF values in patients with heart failure. This model is considered more capable of recognizing complex data patterns, thereby producing better predictive accuracy compared with the Decision Tree algorithm.
Co-Authors AA Sudharmawan, AA Agus Herwanto Ambarwati Anastasia Cyntia Dewi Kurniawati Azizatul Azza Azza, Azizatul Badie Uddin Brian Jodi Daniel Happy Putra Davina Afifah Zahra Dewi Krismawati Dewi, Deasy Rosmala Dewi, Sukmala Dhiar Niken Larasati Dina Sonia Dyah Kurniawati Dyah Kurniawati Eva Rahmawati Firdayana Firdayana Gheary Artha Sitanggang Harna, Harna Hesti Yuniarti Hosizah Hosizah Hosizah Hosizah Hosizah Hosizah Markam Husni Abdul Muchlis Husni Abdul Muchlis Ichwani, Arief Ifah Muzdalifah Indah Cahyani Buana Indawati, Laela Iqbal, Muhammad Fuad Kafida Mey Dhani Rahmat Krismawati, Dewi Kurniawati, Anastasia Cyntia Dewi Larasati , Dhiar Niken Lestari, Betri Widya Linta Ifada Linta Ifada Mahadewi, Erlina Puspitaloka Mauren Michaela, Sarah Michaela, Sarah Mauren Mitha Mandasary Munggaran, Rahayu Putri Muniroh, Muniroh Mustikawati, Intan Silviana Nafs, Tazkyatun Nizirwan Anwar Nori wilantika Nungky Nurkasih Kendrastuti Panutun, Satria Bagus Pramana, Setia Putri, Suci Sri Endah Lestari Mulyanto Putri, Tacyah Kholifah Rafidah, Arlien Rona Rahmawati, Danisa Ocha Rahmayanti R, Andi Rosida, Putri Lailatul Rubina, Tirzhana Jean Ruhmayanti, Nur Ayu Sa'pang, Mertien Sangadji, Namira Wadjir Saputra, Alief Imran Saraswati, Henny Seprianto Septian Bagus Wibisono Silvia Ni'matul Maula Sinlae, Andrey Reynaldi Devada Sulistianingsih Sulistianingsih Sulistianingsih Sulistianingsih Suprobowati Suprobowati Supryatno, Adi Tazkyatun Nafs Temesvari, Nauri Anggita Tita Ardianti Tria Saras Pertiwi Widia Sari Widjaja, Lily Witri Zuama Qomarania Yati Maryati Yosi Siti Nur Azizah Zuama, Witri