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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Kajian Ilmiah Kesehatan Dan Teknologi (JKIKT) Jurnal Kesehatan ARTERI : Jurnal Ilmu Kesehatan Community Development Journal: Jurnal Pengabdian Masyarakat Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Jurnal Abdimas Medika BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) Health & Medical Sciences Manajemen Pelayanan Kesehatan KOPEMAS SEJAGAT : Jurnal Pengabdian Masyarakat Health Media
dony setiawan hendyca putra
politeknik negeri jember
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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HUBUNGAN PERILAKU CARING PERAWAT DENGAN KEPUASAN PASIEN DI RUMAH SAKIT BALADHIKA HUSADA JEMBER Dony Setiawan Hendyca Putra; Hendro Prasetyo; Prestasianita Putri
Jurnal Kajian Ilmiah Kesehatan dan Teknologi Vol. 5 No. 2 (2023)
Publisher : Politeknik Unggulan Kalimantan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52674/jkikt.v5i2.110

Abstract

Patient satisfaction is an important parameter, based on hospital standards that satisfaction is a form of service quality. Some patients are still dissatisfied with the service received. Patient dissatisfaction is caused by the nurse's lack of caring behavior. The purpose of this study was to the determine the relationship beetweem nurse caring behavior and patient satisfaction at Baladhika Husada Jember Hospital. This study used a correlational design with a cross sectional approach.The population this study were patients treated in the Mawar, Flamboyan and Nusa indah rooms for 2 weeks, namely150 pastients and a sample of 109 respondents was obtained. The sample of this research used Non Probabilty with accidental sampling technique. Data processed to use analyse of univariate and bivariate. Statistic test used Spearmank rank. The results of the nurse's caring behavior research were said to be good at 83.5%, sufficient 11% and less by 5.5% while for patient satisfaction who felt satisfied 86.2% and dissatisfied by 13.8%. At statistical test of Spearmank rank test obtained of p value = 0,000 with α value 0,05. These results indicated that there was a relationship between nurse caring behavior and patient satisfaction at Baladhika Husada Jember Hospital.
Analisis Klasifikasi Stadium Kanker Payudara Menggunakan Algoritma Naïve Bayes Berdasarkan Data Rekam Medis Mudafiq Riyan Pratama; Sefia Ayu Maharani; Mochammad Choirur Roziqin; Dony Setiawan Hendyca Putra
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.243

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women, highlighting the need for faster and more accurate approaches to support disease staging. The utilisation of electronic medical records through data mining techniques provides an alternative approach for breast cancer stage classification. This study aimed to analyse breast cancer stage classification using the Naïve Bayes algorithm based on electronic medical record data from patients at Baladhika Husada Level III Hospital, Jember. A quantitative approach was employed using secondary data consisting of 476 breast cancer medical records selected from a total of 1,082 records. The research stages included data selection, data cleaning, categorical encoding, model development using the Naïve Bayes algorithm, and model evaluation using a Confusion Matrix based on accuracy, precision, and recall. Model performance was evaluated using nine training-testing split scenarios ranging from 10:90 to 90:10. The experimental results showed that the 90:10 split scenario achieved the best performance, with an accuracy of 87.50%, precision of 86.36%, and recall of 86.36%. These findings indicate that the Naïve Bayes algorithm is capable of classifying breast cancer stages effectively based on patients' clinical characteristics recorded in electronic medical records. The proposed approach demonstrates the potential of integrating electronic medical records and the Naïve Bayes algorithm to support the development of clinical decision support systems for breast cancer stage classification.
Analisis Faktor Penyebab Ketidaklengkapan Pengisian Resume Medis Rawat Inap Di RSU Bahagia Makassar Zahratul Jannah AT Tabrani; Sabran Sabran; Dony Setiawan Hendyca Putra; Gamasiano Alfiansyah
ARTERI : Jurnal Ilmu Kesehatan Vol 7 No 1 (2025): November
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/arteri.v7i1.716

Abstract

Incomplete medical resume documentation remains a persistent issue, including at Bahagia General Hospital Makassar. In the Cempaka inpatient ward, the percentage of incomplete medical resumes in November, December, and January reached an average of 8.00 and showed an increasing trend. This study aims to analyze the factors contributing to the incompleteness of medical resume documentation based on Armstrong and Baron’s (1998) performance theory. The study employed a qualitative approach through interviews, observations, documentation review, and brainstorming. Research subjects consisted of physicians, nurses, and medical record staff. The findings indicate several contributing factors. Individual factors include the suitability of staff educational backgrounds, the absence of training related to medical resume completion, and limited knowledge regarding completeness standards (SPM). Leadership factors involve the lack of motivational strategies such as rewards for staff who consistently complete medical resumes, as well as insufficient involvement of all staff in completeness evaluations. System factors include suboptimal use of checklist forms, the absence of standardized operating procedures (SOP) for completing and assessing medical resumes, and the lack of SOP socialization. Situational factors are related to workload issues, particularly staff performing multiple roles (double jobs). Meanwhile, group factors were found to function well through effective teamwork among staff. In conclusion, incompleteness in medical resume documentation is influenced by individual, leadership, system, and situational factors. It is recommended to provide training for involved staff, develop and implement SOPs, and conduct SOP socialization to improve medical resume completeness.
Medical Record-Based Prediction of Type 2 Diabetes Mellitus Risk Using the Naïve Bayes Algorithm Mochammad Choirur Roziqin; Nabila Ersa Wulandari; Bakhtiyar Hadi Prakoso; Dony Setiawan Hendyca Putra; Muhammad Ifantara Putra; Gamasiano Alfiansyah
Jurnal Infokes Vol 16 No 2 (2026): Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan
Publisher : Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/gjtc0v25

Abstract

Background: Type 2 diabetes mellitus is a non-communicable disease with a high prevalence and the potential to cause serious complications if not properly managed. Identifying risk factors is an essential step in the prevention and control of this disease. Objective: This study aimed to develop and evaluate a medical record-based prediction model for type 2 diabetes mellitus risk using the Naïve Bayes algorithm and to identify the most influential risk factors among hospitalized patients at Bhayangkara Bondowoso Hospital in 2024. Methods: A descriptive quantitative research design was employed, with data processing conducted using the Naïve Bayes algorithm and RapidMiner software. The sampling technique used was total sampling, resulting in 630 medical records, consisting of 315 patients diagnosed with type 2 diabetes mellitus and 315 non-type 2 diabetes mellitus patients. The analyzed variables included age, sex, body mass index (BMI), hypertension, smoking history, cardiovascular disease history, and family history. Results: The results indicated that the most influential risk factors for type 2 diabetes mellitus were age ?45 years, obesity-level BMI, and a history of hypertension. Conclusion: The Confusion Matrix evaluation with a 95%:5% split ratio produced an accuracy of 90.62%, a precision of 100%, and a recall of 81.25%. Suggestion: Hospitals are encouraged to strengthen health promotion programs regarding type 2 diabetes mellitus risk factors and involve patients’ families in educational interventions to help prevent disease-related complications.
Co-Authors Adinda Zulaikha Aji Galih Pamenang Amelia Herdianti Purnama Angga Rahagiyanto Anggi Maulidya Ari Sukawan Assyifa Itsnainia Mustika Atma Deharja Bakhtiyar Hadi Prakoso Bhakti Aryani Bhre Diansyah D.K Demiawan Rachmatta Putro Mudiono Deva Setia Pratama Diana Barsasella Eden Shella Abia Cayuga Efri Tri Ardianto Ely Mulyadi Erna Selviyanti Errica Rostia Loren Estin Roso Pristiwaningsih Farah Dilla Wulandari Feby Erawantini Feby Erawantini Fi Isatir Rodiyah Futari Ayu Istikomah Gamasiano Alfiansyah Gandu Eko Julianto Suyoso Hendra Lexmana Hendro Prasetyo Hendro Prasetyo Heri Warsito I Gede Wiryawan Ihwan Huda Al Mujib Ike Puspa Adityas Ines Meiyola Pradanthi Jihan Ghina Mumtaza Khafidurrohman Agustianto Kurnia Arofah Mahardika Nugraha Maya Weka Santi Miftahul Jannah Mochammad Arief Darmawan Mochammad Choirur Roziqin Moh Lutfi Rizalul Hakim Moh Zaenal Abidin Mubarot Isa Mudafiq Riyan Pratama Muhammad Ifantara Putra Muhammad Misbahul Muttaqiin Muhammad Yunus Muzaffatul Hasan Nabila Ersa Wulandari Nafa Maharani Najla Kamil Niyalatul Muna Novita Nuraini Nugroho Setyo Wibowo Nur Fitri Rosmayanti Prawidya Destarianto Prestasianita Putri Priscilia Noviyanti Putri Rahayu Ratri Rachmawati, Ervina Rania Ramadhani Sofyan Ratih Putri Damayati Rinda Nurul Karimah, Rinda Nurul Rindiani Rindiani Riski Nurjannah Rizky Farah Dilla Rossalina Adi Wijayanti Sabran Sabran Salma Firyal Nabila Sanuri Istiqamah Sefia Ayu Maharani Selvia Juwita Swari Sustin Farlinda Tegar Wahyu Yudha Pratama Vanny Permata Sari Veronika Vestine Vivi Sumarliyanti Ekowati Sugiyanto WIDATUL WAHIDAH Yuliani Yuliani Zahratul Jannah AT Tabrani