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All Journal Public Health of Indonesia Syntax Literate: Jurnal Ilmiah Indonesia PEDAGOGIA Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Rekam Medis dan Informasi Kesehatan Jurnal Pengabdian Masyarakat AbdiMas JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Indonesian of Health Information Management Journal (INOHIM) Jurnal Kesehatan Masyarakat Celebes PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ners Jurnal Pengabdian Masyarakat Indonesia Journal of Information Systems for Public Health Journal of Baja Health Science Indonesian Journal of Health Information Management Services (IJHIMS) Promotor: Jurnal Mahasiswa Kesehatan Masyarakat
Tria Saras Pertiwi
Universitas Esa Unggul
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PENGUKURAN BEBAN KERJA PEREKAM MEDIS DAN INFORMASI KESEHATAN PASCA PENERAPAN REKAM MEDIS ELEKTRONIK DI INSTALASI REKAM MEDIS RSUD KABUPATEN TANGERANG Ikaningsih, Kurnia Tisna; Pertiwi, Tria Saras; Hosizah, Hosizah; Temesvari, Nauri Anggita
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5874

Abstract

Abstract: Workload is the accumulation of tasks and responsibilities that must be carried out by employees or staff in accordance with the targets set by their supervisors within a specific period of time. The transition from a manual system to an electronic system has the potential to affect the workload of Medical Record and Health Information (MRHI) personnel. This study aims to measure the workload of MRHI staff after the implementation of Electronic Medical Records (EMR) in the Medical Records Department, using job analysis and workload analysis methods based on Regulation of the Ministry of Administrative and Bureaucratic Reform (KEMENPAN RB) No. 1 of 2020. This is a descriptive study with a purposive sampling approach, involving 10 out of 34 MRHI staff with a D3-level education. Data were collected through interviews and direct observation.The results show that the workload for new outpatient registration is 4 minutes, returning outpatients 3 minutes, inpatients 13.4 minutes, and self-registration via APM 5 minutes—requiring 16 staff. Assembling takes 10 minutes per medical record, requiring 2 staff; outpatient coding takes 1 minute and inpatient coding 3.5 minutes per record, requiring 3 coders. Filing and distribution take 6 and 5 minutes per record respectively, requiring 6 staff. Retention and digitization of active and inactive records take 10 minutes per record, requiring 5 staff. Reporting requires 15,240 minutes in total, with 3 reporting staff. Analysis of EMR completeness requires 15 minutes per record, needing 2 expert MRHI staff, while EMR monitoring requires 3 expert MRHI staff. The total workforce required is 35 skilled MRHI personnel and 5 expert MRHI professionals. Keyword: Electronic Medical Records, Workload, Medical Record and Health Information, Job Analysis Abstrak: Beban kerja merupakan akumulasi tugas dan tanggung jawab yang harus dilaksanakan oleh karyawan atau petugas sesuai dengan target yang telah ditentukan oleh atasan dalam periode waktu tertentu. Transisi dari sistem manual ke sistem elektronik berpotensi mempengaruhi beban kerja Perekam Medis dan Informasi Kesehatan (PMIK) Penelitian ini bertujuan untuk mengukur beban kerja PMIK pasca penerapan RME di Instalasi Rekam Medis menggunakan metode analisis jabatan dan analisis beban kerja sesuai Peraturan KEMENPAN RB No. 1 Tahun 2020. Penelitian ini merupakan penelitian deskriptif dengan pendekatan purposive sampling, melibatkan 10 orang dari total 34 petugas di instalasi rekam medis dengan pendidikan D3 PMIK. Pengumpulan data dilakukan melalui wawancara dan observasi. Hasil pengukuran menunjukkan bahwa beban kerja pendaftaran rawat jalan pasien baru 4 menit,pasien lama 3 menit untuk pasien rawat inap 13,4 menit sedangkan APM 5 menit dibutuhkan sebanyak 16 petugas, Beban kerja assembling 10 menit/BRM dibutuhan 2 tenaga assembling, Beban kerja koding pasien rawat jalan 1 menit , koding rawat inap 3,5 menit/BRM di butuhkan 3 tenaga koding, beban kerja filling 6 menit/ BRM dan distribusi 5 Menit/BRM memerlukan 6 petugas, Beban kerja petugas retensi dan alih media berkas rekam medis in akif dan aktif 10 menit/BRM dan dibutuhkan 5 tenaga, Beban kerja pelaporan 15.240 menit kebutuhan tenaga pelaporan 3 petugas , beban kerja analisa kelengkapan rekam medis elektronik 15 menit/RME di butuhkan 2 tenaga PMIK ahli dan monitoring rekam medis elektroni k sebanyak 3 tenaga PMIK ahli, total kebutuhan tenaga PMIK adalah 35 tenaga PMIK terampil dan 5 orang PMIK ahli. Kata Kunci: Rekam Medis Elektronik, Beban Kerja, Perekam Medis dan Informasi Kesehatan, Analisis Jabatan
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
The Influence of System Quality and Information Quality on E-Puskesmas User Satisfaction: An Empirical Study at Kumai Community Health Center Afrelina, Herlina; Pertiwi, Tria Saras; Markam, Hosizah; Qomarania, Witri Zuama
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.6955

Abstract

The implementation of Electronic Medical Records (EMR) in primary healthcare facilities has become a critical component of Indonesia's national digital health transformation agenda. This study examines the influence of system quality and information quality on E-Puskesmas user satisfaction at Kumai Community Health Center in Central Kalimantan Province, Indonesia. Employing a quantitative research approach with cross-sectional design, data were collected from all 58 healthcare staff who directly used the E-Puskesmas system through a validated questionnaire adapted from the DeLone and McLean IS Success Model and End User Computing Satisfaction (EUCS) framework. Multiple linear regression analysis revealed that both system quality (β = 0.408, p = 0.000) and information quality (β = 0.574, p = 0.000) significantly and positively affected user satisfaction, with information quality demonstrating stronger influence. The model achieved an adjusted R² of 0.586, indicating that 58.6% of variance in user satisfaction was explained by these two variables. Descriptive analysis uncovered dimensional heterogeneity, with security and relevance achieving high categories while system reliability, timeliness, and overall satisfaction remained in moderate classification. These findings suggest that healthcare professionals in resource-constrained settings prioritize accurate and relevant patient data over technical system performance, yet infrastructure limitations continue to undermine holistic user experience. The study contributes empirical evidence for improving E-Puskesmas implementation and supports Indonesia's broader digital health integration through the Satusehat platform.
Patient Age: A Determining Factor in Mobile JKN Adoption Arianti, Susi; Markam, Hosizah; Temesvari, Nauri Anggita; Pertiwi, Tria Saras
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.6975

Abstract

The Mobile JKN application represents a significant national initiative to modernize health insurance services in Indonesia, yet adoption rates remain suboptimal despite near-universal population coverage. This study examined the influence of patient demographic characteristics on Mobile JKN utilization for outpatient registration at RSUD Sejiran Setason, a regional hospital in Bangka Belitung. A quantitative cross-sectional study was conducted with 345 respondents selected through accidental sampling. Data were collected using a structured questionnaire measuring four demographic variables (age, gender, education, occupation) and Mobile JKN usage status. Binary logistic regression analysis was employed to determine predictor effects on adoption behavior. Mobile JKN adoption was 38.60%, indicating substantial underutilization. Among demographic predictors, only age demonstrated significant positive influence (p = 0.006, Exp(B) = 1.023), with each additional year increasing adoption likelihood by 2.3%. Gender (p = 0.633), education (p = 0.947), and occupation (p = 0.449) showed no significant effects. The demographic model explained merely 4.4% of variance (Nagelkerke R² = 0.044), suggesting that unmeasured factors substantially determine adoption behavior. Age positively predicts Mobile JKN adoption, contradicting conventional digital divide assumptions, while other demographic characteristics prove insufficient for predicting digital health platform utilization. Implementation strategies should transcend demographic targeting and address systemic, psychological, and technological determinants to achieve equitable digital health transformation in regional Indonesian healthcare settings.
Hubungan Kepuasan Pasien Pada Layanan Rme Dengan Minat Kunjungan Ulang Di Klinik Pratama Upt Layanan Kesehatan ITB Ditya Pratama; Hosizah Hosizah; Witri Zuama; Tria Saras Pertiwi
Jurnal Ners Vol. 10 No. 2 (2026): APRIL 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v10i2.51509

Abstract

Pesatnya perkembangan teknologi di dunia kesehatan menuntut fasilitas pelayanan kesehatan berinovasi dalam meningkatkan kepuasan. Kepuasan pasien berpengaruh pada minta berkunjung kembali ke fasilitas pelayanan kesehatan. Setelah diselanggarakan RME persentase kunjungan pasien di Klinik Pratama UPT Layanan Kesehatan ITB mengalami penurunan sebesar 19,44%. Penurunan disebabkan pasien harus beralih dari sistem manual menjadi elektronik yaitu pasien wajib melakukan reservasi online yang dianggap menyulitkan. Tujuan penelitian untuk mengetahui hubungan kepuasan pasien pada layanan RME dengan minat kunjungan ulang di Klinik Pratama UPT Layanan Kesehatan ITB. Jenis penelitian observasional dengan menggunakan desain cross sectional. Populasi pada penelitian ini adalah rata rata kunjungan pasien klinik 124 pasien perhari. Sampel diambil menggunakan non probability sampling yaitu 95 pasien. Pengumpulan dan analisis data dengan menyebar kueisioner analisis data dan uji chi-square serta Three Box Method. Pada penelitian ini menunjukkan adanya pasien yang tidak puas pada layanan RME yaitu 46 (48,4%) responden sedangkan yang menilai puas yaitu sebanyak 49 (51,6%) responden. Pasien yang menilai tidak berminat yaitu 47 (49,5%) responden sedangkan yang menilai berminat yaitu sebanyak 48 (50,5%) responden. Berdasarkan hasil uji hipotesis terdapat hubungan kepuasan pasien pada layanan RME dengan minat kunjungan ulang dengan hasil nilai p 0,000 (α <0,05) dengan nilai OR 22,76. Maka, klinik perlu meningkatkan layanan RME.
Co-Authors AA Sudharmawan, AA Afrelina, Herlina Alifah Syibilla Kesiha AMELIA FEBRIANA, AMELIA Annisa Oktarini Arianti, Susi Davina Affah Zahra De Vita Enjelin Dewi, Sukmala Ditya Pratama Dyah Kurniawati Elistia, Elistia Fajri, Rahayu Nurul Fariz Satya Hadi Herwanto, Agus Hosizah Hosizah Husni Abdul Muchlis Ichwani, Arief Ikaningsih, Kurnia Tisna Kurniawati, Anastasia Cyntia Dewi Megawati Muda, Cut Alia Keumala Nafs, Tazkyatun Nurmalasari, Mieke Putu Gita Maya Widyaswari Mahayasih Rafi Perdana Tamher Sa'pang, Mertien Salsabilla, Jihan Saskia Salshabila, Andini Dwi Saputra, Alief Imran Silviana Dewi Sri Teguh Rahayu, Sri Teguh Sunarti Supryatno, Adi Sutanto, Imam Temesvari, Nauri Anggita Widodo, Agus Widodo Witri Zuama Witri Zuama Qomarania