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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Intelektualita: Keislaman, Sosial, dan Sains POSITIF Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Ilmiah Matrik INOVTEK Polbeng - Seri Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Global JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Mantik Jurnal Muara Ilmu Ekonomi dan Bisnis Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) International Journal of Advanced Science Computing and Engineering Bulletin of Social Informatics Theory and Application
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Journal : POSITIF

Analisa Rekam Medis Elektronik Untuk Menentukan Diagnosa Medis Dalam Kategori Bab ICD 10 Menggunakan Machine Learning Amin, Zulius Akbar; Cholil, Widya; Herdiansyah, M. Izman; Negara, Edi Surya
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 7 No 2 (2021): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v7i2.1140

Abstract

Based on observations of the business process flow at the Siti Fatimah Hospital, the background for this study was the medical record document and ICD-10 code which was carried out manual diagnosis, making it difficult for the medical record section in the proper and fast CHAPTER arrangement of the ICD-10 code. The International Statistical Classification of Diseases and Related Health Problems (ICD) can be used to calculate or record a valid patient history of hospitalization. The Cross-Industry Standard Process For Data Mining (CRISP-DM) method is used in this study to become a strategy to describe the problem in general from the domain or research unit. While the machine learning algorithm for multiclass classification uses the Naïve Bayes algorithm, Support Vector Machine, Logistic Regression to create a diagnostic model for medical action. This study predicts ICD-10 chapter categories from medical action records from electronic medical records. With this research, it is hoped that machine learning can facilitate the medical record section in predicting the ICD-10 chapter category by analyzing electronic medical record data using the Chapter ICD-10 Decision Support System information system
Analisa Rekam Medis Elektronik Untuk Menentukan Diagnosa Medis Dalam Kategori Bab ICD 10 Menggunakan Machine Learning Zulius Akbar Amin; Widya Cholil; M. Izman Herdiansyah; Edi Surya Negara
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 7 No 2 (2021): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v7i2.1140

Abstract

Based on observations of the business process flow at the Siti Fatimah Hospital, the background for this study was the medical record document and ICD-10 code which was carried out manual diagnosis, making it difficult for the medical record section in the proper and fast CHAPTER arrangement of the ICD-10 code. The International Statistical Classification of Diseases and Related Health Problems (ICD) can be used to calculate or record a valid patient history of hospitalization. The Cross-Industry Standard Process For Data Mining (CRISP-DM) method is used in this study to become a strategy to describe the problem in general from the domain or research unit. While the machine learning algorithm for multiclass classification uses the Naïve Bayes algorithm, Support Vector Machine, Logistic Regression to create a diagnostic model for medical action. This study predicts ICD-10 chapter categories from medical action records from electronic medical records. With this research, it is hoped that machine learning can facilitate the medical record section in predicting the ICD-10 chapter category by analyzing electronic medical record data using the Chapter ICD-10 Decision Support System information system