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ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
Published by Institut Teknologi Nasional Bandung
ISSN : 23388323     EISSN : 24599638     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering Engineering
Jurnal ELKOMIKA diterbitkan 3 (tiga) kali dalam satu tahun pada bulan Januari, Mei dan September. Jurnal ini berisi tulisan yang diangkat dari hasil penelitian dan kajian analisis di bidang ilmu pengetahuan dan teknologi, khususnya pada Teknik Energi Elektrik, Teknik Telekomunikasi, dan Teknik Elektronika.
Arjuna Subject : -
Articles 21 Documents
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Search results for , issue "Vol 12, No 1: Published January 2024" : 21 Documents clear
Kendali Aliran dan Tekanan Adaptif dengan Metode Artificial Neural Network pada Alat Terapi Oksigen SALAM, ABYANUDDIN; NUGRAHA, NUR WISMA; ALFARIDHANI, WILDAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 1: Published January 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i1.133

Abstract

ABSTRAKPenelitian ini bertujuan untuk merancang prototype pengendalian aliran dan tekanan adaptif pada alat terapi oksigen. Sensor yang digunakan yaitu sensor MAX30100 untuk membaca saturasi oksigen dan sensor MLX90614 sebagi sensor yang dapat menghitung Respiration Rate atau laju napas. Metode yang digunakan yaitu Artificial Neural Network yang diimplentasikan pada Raspberry Pi. Sistem akan bekerja dengan memprediksi nilai laju aliran dan tekanan oksigen yang diperlukan pasien berdasarkan nilai Respiration Rate (RR). Artificial Neural Network (ANN) dapat diimplmentasikan pada rancangan alat terapi oksigen, dengan persentase akurasi Output ANN terhadap perhitungan yaitu 99,39%, sedangkan persentase akurasi ANN terhadap pembacaan aliran oksigen yang terbaca pada sensor flow sebesar yaitu 94,73% dan persentase akurasi ANN terhadap pembacaan tekanan oksigen pada sensor pressure sebesar 89,03%.Kata kunci: Terapi Oksigen, Respiration Rate, Artificial Neural Network ABSTRACTThis research aims to design a prototype of flow and pressure control in an adaptive oxygen therapy device. The sensors used are MAX30100 sensors to read oxygen saturation and MLX90614 sensors as sensors that can calculate Respiration Rate or breath rate. The method used is Artificial Neural Network which is implemented on Raspberry Pi. The system will work by predicting the value of the flow rate and oxygen pressure required by the patient based on the Respiration Rate (RR) value. Artificial Neural Network (ANN) can be implemented in the design of oxygen therapy devices, with the percentage of ANN Output accuracy to the calculation of 99.39%, while the percentage of ANN accuracy on oxygen flow readings on the flow sensor is 94.73% and the percentage of ANN accuracy on oxygen pressure readings on the pressure sensor is 89.03%.Keywords: Oxygen Therapy, Respiration Rate, Artificial Neural Network

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