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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Media Kesehatan Masyarakat Indonesia NUTRIRE DIAITA Jurnal Kebijakan Kesehatan Indonesia UNEJ e-Proceeding JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Public Health of Indonesia Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Kesehatan Medika Saintika Jurnal Ilmiah Bidan Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Window of Health : Jurnal Kesehatan JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Manajemen Informatika Jurnal Kesehatan Indonesia Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Indonesian of Health Information Management Journal (INOHIM) Health Information : Jurnal Penelitian PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ners Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Journal of Nursing and Public Health (JNPH) Indonesian Journal of Global Health research Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Jurnal Health Sains Jurnal Kesehatan Tambusai J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan MAHESA : Malahayati Health Student Journal Health Publica : Jurnal Kesehatan Masyarakat International Journal of Science and Society (IJSOC) AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Sehat Rakyat: Jurnal Kesehatan Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sosial dan Sains 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) Detector: Jurnal Inovasi Riset Ilmu Kesehatan Promotor: Jurnal Mahasiswa Kesehatan Masyarakat Al Makki Health Informatics Journal E-Amal: Jurnal Pengabdian Kepada Masyarakat Holistik Jurnal Kesehatan Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis Jurnal Informatika Dan Tekonologi Komputer
Hosizah Hosizah
Universitas Esa Unggul
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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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.
FACTORS THAT INFLUENCE MENTAL HEALTH INFORMATION-SEEKING BEHAVIOR AMONG GENERATION Z Gita Fitrisia; Hosizah Hosizah
JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Vol 10, No 2 (2025)
Publisher : Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jipi.v10i2.25893

Abstract

Generation Z, which is vulnerable to mental health problems, uses the internet as a primary source for obtaining information related to mental health. This study aims to determine the influence of demographic characteristics, digital literacy, and psychological conditions on mental health information-seeking behavior among Generation Z. This study used a quantitative approach with a cross-sectional design. Data collection was carried out by distributing online questionnaires consisting of a questionnaire on respondents' demographic characteristics, a digital literacy questionnaire adapted from the Indonesian Ministry of Communication and Information Technology (KOMINFO) questionnaire for measuring the 2022 Indonesian digital literacy index, DASS-42 to measure psychological conditions, and a questionnaire to measure mental health information-seeking behavior that has been tested for validity and reliability. Data analysis used descriptive analysis, classical assumption tests, and hypothesis testing using multiple linear regression tests. Gender and digital literacy variables partially influence mental health information-seeking behavior. Meanwhile, simultaneously, it was found that demographic characteristics, digital literacy, and psychological conditions significantly influence mental health information-seeking behavior among Generation Z by 42.1%, while the remaining 57.9% was influenced by other factors not examined in this study. The results of this study are expected to be a basis for madrasas in designing programs to improve digital literacy as well as awareness and handling of mental health problems among students.
The Influence of System Quality and Information Quality on E-Puskesmas User Satisfaction: An Empirical Study at Kumai Community Health Center Herlina Afrelina; Tria Saras Pertiwi; Hosizah Markam; Witri Zuama Qomarania
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

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 Susi Arianti; Hosizah Markam; Nauri Anggita Temesvari; Tria Saras Pertiwi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

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 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.
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.
Hubungan Kualitas Resume Medis dan Ketepatan Koding Kasus Pneumonia dengan Hasil Klaim di RSIJ Sukapura Sansy Dua Lestari Putri Azah; Husni Abdul Muchlis; Hosizah Hosizah; Witri Zuama Qomarania
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.v7i3.5839

Abstract

Hospitals submit BPJS Health insurance claim documents to obtain reimbursement for healthcare services provided. Observations at RSIJ Sukapura identified 32 pending claim files (70%) caused by incomplete documentation of clinical symptoms in medical discharge summaries. This issue led to the revision of the diagnosis code from J18.9 to J22, resulting in reduced claim values and an increased number of pending claims. This study aimed to examine the relationship between the quality of medical discharge summaries, coding accuracy, and claim outcomes among hospitalized pneumonia patients at RSIJ Sukapura. A quantitative study was conducted using a population of 102 pneumonia cases recorded in May 2024. Using purposive sampling, 89 claim files were selected for analysis. The results showed that 71 files (79.8%) had good-quality medical discharge summaries, 75 files (84.3%) demonstrated accurate coding, and 70 files (78.7%) resulted in eligible claims. Multiple logistic regression analysis revealed a significant relationship between the quality of medical discharge summaries, coding accuracy, and claim outcomes (p < 0.001). Good-quality documentation and accurate coding increased the likelihood of producing eligible claims. Therefore, physician education regarding complete documentation in accordance with BPJS Health requirements is recommended to reduce pending claims.
Instrumen Audit Kualitas Rekam Medis Elektronik Menggunakan Content Validity Index (CVI) Yuniana Eka Pratiwi; Hosizah Hosizah; Sri Jumiati Agustina
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 4 (2026): September (Issue in Progress)
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i4.6748

Abstract

Although the National Brain Center Hospital Prof. Dr. Mahar Mardjono Jakarta has implemented an Electronic Medical Record (EMR) system, the quality of its data has not yet met the completeness standards established by the Indonesian Ministry of Health. As the hospital's most prevalent condition, ischemic stroke requires accurate and complete medical documentation to support effective clinical decision-making. Therefore, a comprehensive EMR audit instrument is needed to systematically evaluate data quality. This study aimed to develop and validate an EMR data quality audit instrument for ischemic stroke patients. A Research and Development (R&D) design was employed using the Data Quality Management (DQM) framework based on the data quality characteristics defined by the American Health Information Management Association. The instrument was validated by six experts using the Content Validity Index (CVI), including the Item-Level Content Validity Index (I-CVI) and the Scale-Level Content Validity Index (S-CVI), with indicators derived from the ischemic stroke clinical pathway. The validation results showed I-CVI values ranging from 0.83 to 1.00 and an S-CVI value of 0.99, indicating excellent content validity. The instrument is therefore considered highly valid and suitable for systematic EMR data quality audits to improve the quality of medical documentation.
Co-Authors Agustin, Karisna Agustin Alita Sandy Putriani Alumni STIKES Husada Borneo Amanda Ambarwati Ambarwati Ambarwati Anastasia Cyntia Dewi Kurniawati Andani, Evi Andry Arief Ichwan Aris Susanto Atmojowati, Fitria Ayuningtyas, Meta Azza, Azizatul Badie Uddin Basuki Hari Brian Jodi Darmawan, Lusiana Davina Afifah Zahra Dedy Kurniawan desi aryani Desi Aryani Desviyanti Eka Saputri Dewi Krismawati Dewi, Sukmala Dhiar Niken Larasati Dian Yuliani Dita Manisha Amrina Putri Ditya Pratama Duta Liana Dyah Kurniawati Endang Titi Amrihati, Endang Titi Eva Rahmawati Fachmi Tamzil Firdayana Firdayana Gita Fitrisia Hari Basuki Harna, Harna Hera Adrianti Herey, Peter Herlina Afrelina Herliza Husni HILHAMI, HILHAMI Husni Abdul Muchlis Husni, Herliza Ichwan, Arief Ichwani, Arief Ida Putri S Rospita Ifah Muzdalifah Ifah Muzdalifah Ikaningsih, Kurnia Tisna Indah Cahyani Buana Irmawan, - Ismi Azizah Jayanti Aswinasih Jayanti Lestari Jihan Saskia Salsabilla Julianti, Anissa Julita D. L. Nainggolan JUS’AT, IDRUS Kholida Syiah Nasution Krismawati, Dewi Kuntoro Kuntoro Kuntoro Kuntoro Kurnia Tisna Ikaningsih Kurniawati, Anastasia Cyntia Dewi Larasati , Dhiar Niken Latumapina, Stella Florence Imanuela Lazuardy, Achmad Sirri Lepong, Maria Lestari, Jayanti Lisda Novilia Lusiana Darmawan maria Hiasinta Meo Marzo, Roy Rillera Mauren Michaela, Sarah Menna, Yasinta Rosalia Meo, maria Hiasinta Michaela, Sarah Mauren Mitha Mandasary Mulyo Wiharto Munggaran, Rahayu Putri Mustikawati, Intan Silviana Muzdalifah, Ifah Nadri Aulia Fathia Nanda Dina Cahya Nauri Anggita Temesvari Nisa Rafilda Khafidah Nofierni Nofierni Novi Mulyani Putri Nungky Nurkasih Kendrastuti Nurhasanah, Raden Nurmalasari, Mieke Oktavianti, Putri Panutun, Satria Bagus Peter Herey Peter Herey Purba, Andrea Krisler Purwanti, Amalia Indah Puspita, Kori Putri, Tacyah Kholifah Qomarania, Witri Uama Rafidah, Arlien Rona Rahmawati Rahmawati Rahmawati, Danisa Ocha Rahmayani, Kamila Zainab Ramadhani, Rizky Ramdhan, Yanuar Regy Permata Sari Resia Perwirani Rina Anindita Rina Mutiara Rina Mutiara, Rina Rina Yuliana Rischa Zahra Bellanisa Rosida, Putri Lailatul Rospita, Ida Putri S Rubina, Tirzhana Jean Rusman Efendi Salsabilla, Jihan Saskia Salshabila, Andini Dwi Sansy Dua Lestari Putri Azah Saqil Ahmad Saragih, Pestaria Sari Dewi Lamsir Seiswati, Siswati Sella Yossiant Selviani, Selviani Sihombing, Apriliana Silalahi, Rani Gartika Holivia Sinlae, Andrey Reynaldi Devada Siswati Siswati Seiswati Sri Jumiati Agustina Sri Rahayu STIKES Husada Borneo Sulistianingsih Sulistianingsih Sunarti Susi Arianti Sutanto, Imam Temesvari, Nauri Anggita Tita Ardianti Tria Saras Pertiwi Umi Khoirun Nisak Widjaja, Lily Widodo, Agus Widodo Witri Uama Qomarania Witri Zuama Witri Zuama Qomarania Witri Zuama Qomarania Wittri, Zalipa Yani Haida Shanti Yati Maryati Yati Maryati Yoga Utomo Yosi Siti Nur Azizah Yulia, Noor Yuliani, Dian Yuniana Eka Pratiwi Yunita Fauzia A. Yunita Fitri Widiyawati Yusnaeni Yusnaeni Yusnaeni Yusnaeni, Yusnaeni Zuama, Witri