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All Journal Journal Zetroem Journal of Robotics, Automation, and Electronics Engineering
Nancy Febriani Taek
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Alat Peringatan Pelanggaran Physical Distancing Berbasis Raspberry Pi sebagai Upaya Preventif Penyebaran Covid-19 pada Era New Normal Hilal Fahrul Hamam; Irwan Ardiansyah; Burhan Dwi Ardiandyah; Dira Tri Puspita; Nancy Febriani Taek; Ardy Seto Priambodo
ZETROEM Vol 3 No 2 (2021): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v3i2.1494

Abstract

The Covid-19 pandemic that hit Indonesia forced the Indonesian population to be ready with a new normal order. With this condition, it is hoped that the community can implement changes to the new order of life by implementing health protocols. But nowadays, people often violate health protocols, physical distancing, which is one form of prevention in the new normal era. The innovation of a Raspberry Pi-based tool designed to produce warnings for physical distancing violations is expected to minimize violations. The camera installed on the system will help detect the distance between two or more people and will give a warning to immediately carry out physical distancing if the measured distance is less than 1 meter. The purpose of writing this research is to design a system and concept of "Preventive Efforts for the Spread of Covid-19 Through a Raspberry Pi-Based Distance Monitoring System". The method of writing this article uses the concept of a literature study involving several studies and scientific findings in the form of secondary data from research results that have been published in scientific journals. From the results of the tests that have been carried out, it is known that the tool has succeeded in detecting physical distancing violations between two or more people. After the tool can detect a violation, the tool emits a warning sound to inform the violation that has occurred. From the result, it can be concluded that this system proposed can run well as expected.
Design and Implementation of a Student Counting and Monitoring System in a Laboratory Using Human Tracking Method with OpenCV and TensorFlow Nancy Febriani Taek; Arya Sony
Journal of Robotics, Automation, and Electronics Engineering Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v2i1.554

Abstract

Laboratories serve as crucial facilities supporting practical activities, with a recommended maximum of 20 students, necessitating periodic monitoring to count the dynamic number of students within. The system utilizes the COCO dataset labeled ”person,” involving an approach with entry and exit preference lines, ID identification implementation, and object detection models YOLO v3 Tiny and Faster R-CNN ResNet50. The main system components, Raspberry Pi 3 Model B+, Raspberry Pi Camera 5 MP (f/1.3), and Raspberry Pi 7-inch Touch Display, are integrated for processing, real-time video recording, and image display functions. Test and evaluation results reveal that YOLO v3 Tiny achieves an 88.24% accuracy for entry counting and 75% for entry-exit counting, with an average processing rate of 4.89 FPS, while Faster R-CNN ResNet50 demonstrates lower accuracy, reaching 70.59% and 45.83%, with an average processing rate of 0.58 FPS.
Co-Authors Ardy Seto Priambodo Arya Sony Burhan Dwi Ardiandyah Dira Tri Puspita Hilal Fahrul Hamam Irwan Ardiansyah