Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Elisabeth Agustina
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sistem Deteksi Hipoksia menggunakan Metode Decision Tree berdasarkan Detak Jantung dan Kadar Oksigen Elisabeth Agustina; Edita Rosana Widasari; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hypoxia is a condition where there is a continuous lack of oxygen either in the short or long term according to a period. If hypoxia occurs in the short term, what is caused is acute hypoxia. The symptoms found in hypoxia are very fast heartbeat (tachycardia), rapid breathing, dizziness, weakness. Based on the existing problems, the authors want to conduct research to detect hypoxia. The tool designed for this study can detect hypoxia based on heart rate and oxygen saturation. This detection is very easy to use, just by placing your index finger on the MAX30100 infrared sensor, after that you get the measurement results which will be displayed on the LCD screen. The results of these measurements will be the input data for classification. This classification uses the Decision Tree method where this method is very accurate and fast in carrying out the classification process. Classification results will be displayed on the LCD screen. Testing of this tool was carried out 10 trials in detecting heart rate and oxygen saturation. The accuracy obtained when doing the classification is 100%. The MAX30100 sensor when measuring heart rate obtains an accuracy of 97.66% and an error rate of around 2.34. Then, the accuracy obtained when detecting oxygen levels is 98.75% with an error rate of 1.25%
Co-Authors Dahnial Syauqy Widasari, Edita Rosana