eprintid: 8868
rev_number: 7
eprint_status: archive
userid: 46
dir: disk0/00/00/88/68
datestamp: 2025-01-16 02:25:03
lastmod: 2025-01-16 02:25:03
status_changed: 2025-01-16 02:25:03
type: thesis
metadata_visibility: show
creators_name: Kumas, Nurmin
creators_name: Chairunnas, Andi
creators_name: Ardiansyah, Deden
creators_NPM: 065118322
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Kumas, Nurmin
contributors_name: Chairunnas, Andi
contributors_name: Ardiansyah, Deden
corp_creators: Universitas Pakuan
corp_creators: Fakultas Matematika dan Ilmu Pnegetahuan Alam
corp_creators: Program Studi Ilmu Komputer
title: Model Sistem Pengenalan Rambu Lalu Lintas Menggunakan Raspberry PI Berbasis Internet Things
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: public
abstract: Traffic Sign Recognition System Model Using Raspberry Pi Based on the
Internet of Things
Andi Chairunnas¹, Deden Ardiansyah², Nurmin Kuma³
¹,²,³ Department of Computer Science, Faculty of Mathematics and Natural Sciences,
Universitas Pakuan, Bogor, West Java, 16143, Indonesia
Abstrak
This research develops a traffic sign recognition system using Internet of
Things (IoT) technology with Raspberry Pi as the main processing unit. The system
aims to help drivers detect traffic signs and improve road safety by providing audio
descriptions of the detected signs. Using OpenCV for image processing, the system
achieves 85% accuracy for clear images and 77% accuracy for blurry images,
demonstrating the potential of IoT-based systems in smart driving applications.
Keywords : IoT; Traffic Sign Recognition; Raspberry Pi; Computer Vision;
OpenCV
date: 2024-10-08
date_type: published
pages: 19
institution: Universitas Pakuan
department: Fakultas Matematika dan Pengetahuan Alam
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Kumas, Nurmin and Chairunnas, Andi and Ardiansyah, Deden  (2024) Model Sistem Pengenalan Rambu Lalu Lintas Menggunakan Raspberry PI Berbasis Internet Things.  Skripsi thesis, Universitas Pakuan.   
document_url: http://eprints.unpak.ac.id/8868/1/Laporan_Skripsi_Nurmin%20kuma.pdf