Article Per Year (5 Year)
p-Index From 2021 - 2026
4.765
P-Index
This Author published in this journals
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 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) J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan KESANS : International Journal of Health and Science NUSRA: Jurnal Penelitian dan Ilmu Pendidikan PELS (Procedia of Engineering and Life Science) Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) 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) International Journal of Health and Information System (IJHIS) Jurnal Bioteknologi & Biosains Indonesia (JBBI) Jurnal Bioteknologi & Biosains Indonesia
Nurmalasari, Mieke
Universitas Esa Unggul Jakarta
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 Nursing Physics Public Health Social Sciences Veterinary Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
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 (Issue in Progress)
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.
Peningkatan Kapasitas PMIK Dalam Mengolah dan Menganalisis Data Klaim INA-CBG’s untuk Meningkatkan Akurasi Kodefikasi & Dokumentasi di RSIJ Pondok Kopi Muchlis, Husni Abdul; Nurmalasari, Mieke; Qomarania, Witri Zuama; Kurniawati, Anastasia Cyntia Dewi; Iqbal, Muhammad Fuad; Maryati, Yati
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 9, No 2 (2026): Volume 9 Nomor 2 (2026)
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v9i2.23624

Abstract

ABSTRAK Keberhasilan klaim JKN-BPJS sangat bergantung pada akurasi dokumentasi klinis dan kodefikasi diagnosis serta prosedur. RSIJ Pondok Kopi masih menghadapi kendala dalam pemanfaatan data klaim untuk evaluasi mutu dan optimalisasi nilai klaim. Kegiatan ini bertujuan meningkatkan kemampuan PMIK dalam mengolah dan menganalisis data klaim INA-CBG’s untuk mendukung akurasi kodefikasi dan dokumentasi. Program dilaksanakan melalui sosialisasi, pelatihan teknis Excel PivotTable, dan pendampingan penyusunan dashboard analisis klaim. Evaluasi menggunakan pre-test dan post-test serta observasi produk analisis. Pelatihan meningkatkan skor pengetahuan tim sebesar 22 poin, terutama pada materi MCC/CC. Tim berhasil menghasilkan dua laporan dashboard internal terkait capture rate MCC/CC dan distribusi LOS (AMLOS/GMLOS). Peningkatan kompetensi PMIK dalam analisis data klaim mampu memperbaiki akurasi kodefikasi dan mendorong pemanfaatan data klaim sebagai alat manajemen mutu dan optimalisasi nilai klaim rumah sakit. Kata Kunci: Data Klaim, Dokumentasi Klinis, INA-CBG’s, Klaim BPJS, Kodefikasi.  ABSTRACT The success of JKN–BPJS claims strongly depends on the accuracy of clinical documentation and clinical coding of diagnoses and procedures. RSIJ Pondok Kopi still faces challenges in utilizing claim data to evaluate service quality and optimize reimbursement values. This community service activity aims to improve the capacity of Health Information Management professionals (PMIK) in processing and analyzing INA-CBG’s claim data to support accurate coding and clinical documentation. The program was carried out through socialization, technical training using Excel PivotTable, and mentoring in developing analytical dashboards. Evaluation was conducted using pre-test and post-test assessments, as well as observation of the analytical products. The training improved staff knowledge by 22 points, with the highest increase in MCC/CC competence. The team successfully produced two internal dashboard reports related to MCC/CC capture rate and LOS distribution (AMLOS/GMLOS). Improving PMIK competency in claim data analysis enhances the accuracy of clinical coding and encourages the use of claim data as a tool for quality management and reimbursement optimization in hospitals. Keywords: Claim Data, Clinical Coding, Clinical Documentation, INA-CBG’s, BPJS Claim.
Convolutional Neural Networks-Based Deep Learning for Diabetic Retinopathy Detection Nurmalasari, Mieke; Kurniawati, Anastasia Cyntia Dewi; Herwanto, Agus; Kurniawati, Dyah; Muchlis, Husni Abdul; Pertiwi, Tria Saras
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 | DOI: 10.24014/ijaidm.v9i1.38631

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
Co-Authors AA Sudharmawan, AA Azizatul Azza Azza, Azizatul Badie Uddin Dewi, Deasy Rosmala Dewi, Sukmala Dyah Kurniawati Firdayana Firdayana Harna, Harna Herwanto, Agus Hosizah Hosizah Husni Abdul Muchlis Ichwani, Arief Indawati, Laela Iqbal, Muhammad Fuad 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 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, Seprianto Septian Bagus Wibisono Silvia Ni'matul Maula Sinlae, Andrey Reynaldi Devada Sulistianingsih Sulistianingsih Suprobowati Suprobowati Supryatno, Adi Temesvari, Nauri Anggita Tita Ardianti Tria Saras Pertiwi Widia Sari Widjaja, Lily Witri Zuama Qomarania Yati Maryati Zuama, Witri