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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Technologia: Jurnal Ilmiah Media Informatika Journal of Computer Networks, Architecture and High Performance Computing Jurnal Responsif : Riset Sains dan Informatika Jurnal Sains Manajemen INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Malcom: Indonesian Journal of Machine Learning and Computer Science SisInfo : Jurnal Sistem Informasi dan Informatika Innovative: Journal Of Social Science Research Journal of Social Science Utilizing Technology TGO Journal of Community Development Journal of International Multidisciplinary Research Jurnal Intelek Dan Cendikiawan Nusantara Technologia Journal Jurnal Sains, Teknologi dan Kesehatan Journal of Social Science Utilizing Technology Jurnal Informatika dan Kesehatan Journal of Public Health Indonesian Journal of Public Health Indonesian Technologia Journal
Agung Rachmat Raharja
Universitas Bandung
Religion Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Transportation Other

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Analysis of Storage Spaces to Support the Health Service System at Santosa Hospital Bandung Central in 2021 Tiny Rahayu; Encep Yayat; Agung Rachmat Raharja
Journal of Public Health Indonesian Vol. 1 No. 1 (2024): MEI-JHH
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/wexm1x97

Abstract

One of the health services provided by the hospital is the medical record service. The implementation of good medical file storage through systematic procedures is one of the keys to the success of health service, it is also one of this study was to determine the medical record storage space, the obstacles that occurred, and the effort made in the storage room at the Santosa Hospital Bandung Central. The research method used is descriptive qualitative research, by describing the relevant data. Data collection techniques in the form of interviews and document studies. From the results of the study it can be said that the layout and environment for storing medical records at Santosa Hospital Bandung Central is appropriate, the number of visits that continues to increase is not comparable to the medical record storage space contained in the storage room, the use of medical records is an effort made by Santosa Hospital Bandung Central. The use of electronic medical records overcomes problems in the storage space so as to minimize disruption of health services provided by the Santosa Hospital Bandung Central to patients.
Penerapan Algoritma Decision Tree dalam Klasifikasi Data “Framingham” Untuk Menunjukkan Risiko Seseorang Terkena Penyakit Jantung dalam 10 Tahun Mendatang Agung Rachmat Raharja; Jayadi; Angga Pramudianto; Yoki Muchsam
Technologia Journal Vol. 1 No. 1 (2024): Tecnologia Journal-February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/cwgzp962

Abstract

Heart disease is one of the deadliest diseases in Indonesia and in the world, many of the sufferers do not know the risk of heart disease. This research will analyze and identify factors that will affect the risk of heart disease. With the decision tree algorithm approach which is one of the methods of machine learning that can produce predictive models based on a series of logical decisions. The application is done by classifying framingham data to assess the risk of heart disease in the next 10 years. The result is a Decision Tree model used to predict the risk of heart disease based on the Framingham dataset. The model achieved 74% accuracy on the test data
Co-Authors Al Husaini, Muhammad Al-Husaini, Muhammad Ali Akbar Rismayadi Ali Akbar Rismayadi, Ali Akbar Angga Pramudianto Angga Pramudianto Ari Purno Wahyu Wibowo Cahaya Putra, Vito Hafizh Dodi Syaripudin Dola Ramalinda Drajat, Devi Yuniati Eko Hariyadi Encep Yayat Encep Yayat Erwis, Fauzi Febrianto, Rudhi Wahyudi Hafizh Cahaya Putra, Vito Hariyanti, Ifani Husaini, Al - Imanita Septian Rusdianti Jayadi Jayadi Jayadi Jayadi Jayadi Jayadi Jixiong, Cai Lukmana, Hen Hen Maulinnisaa Tiur R.N Muchsam, Yoki Rahayu, Novi Rismawati Rismawati Riyanto Setiyono, Riyanto Salman Agustiwan Akmal Sandini, Dwi Sindrawati Sindrawati Solihin Solihin Sucipto, Bambang Sutisna, Tori Tiny Rahayu Tiur R.N, Maulinnisaa Turuy, Seh Yuniati Drajat, Devi Zebua, Rony Sandra Yofa