Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization
Muhammad Pandu Dwi Cahyo
Computer Engineering Study Program, Informatics Department, Faculty of Computer Science, Brawijaya University, Indonesia
Computer Science & IT

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

Found 1 Documents
Search

 1 
Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB Muhammad Pandu Dwi Cahyo; Fitri Utaminingrum
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.785

Abstract

The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.
Co-Authors Utaminingrum, Fitri