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All Journal Jurnal Informatika dan Teknik Elektro Terapan Faktor Exacta Jurnal Teknik Elektro Uniba (JTE Uniba) Jurnal UNITEK Jurnal Responsif : Riset Sains dan Informatika Computer Science (CO-SCIENCE) TIERS Information Technology Journal Digital Transformation Technology (Digitech) Jurnal Ilmiah Teknik Elektro
Raden Deasy Mandasari
Universitas Bina Sarana Informatika
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Library & Information Science

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Journal : Jurnal Teknik Elektro Uniba (JTE Uniba)

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Inovasi Parkir Cerdas: Prediksi Posisi Kendaraan dengan Teknologi RFID dan Arduino Mega 2560 R3 Raden Deasy Mandasari; Hartana Hartana
Jurnal Teknik Elektro Uniba (JTE UNIBA) Vol. 9 No. 1 (2024): JTE UNIBA (Jurnal Teknik Elektro Uniba)
Publisher : Lembaga Penelitian Universitas Balikpapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/jteuniba.v9i1.235

Abstract

Urban parking management is an urgent challenge in an ever-increasing vehicle mobility era. This research develops an intelligent parking system that integrates RFID (Radio-Frequency Identification) technology and Arduino Mega 2560 R3 to predict the vehicle's position in real time. The initial literature study identified various approaches to parking management, but none comprehensively combined RFID and Arduino Mega 2560 R3. Therefore, this study fills this gap. The research method involves literature study, system design, hardware and software development, and field testing. In the proposed system, vehicles are equipped with RFID cards, and the parking infrastructure is equipped with RFID readers. When the vehicle enters the parking area, the system reads the RFID card and predicts available parking positions. The results of field tests show an increase in the efficiency of parking space use of around 37% to 46%, and the accuracy of parking position prediction is above 90%. This research provides effective solutions for urban parking management, reducing traffic congestion and air pollution. This system also increases the driver's comfort in finding a suitable parking space. Thus, this research has the potential to support more sustainable urban development by optimizing the use of parking lots and reducing the negative impact of motorized vehicles in urban areas.
Co-Authors Adam Maulidi Kurnia Andi Rosano Darusalam, Azzam Firdaus Fauzi, Krisna Rizki Hartana Hartana Hartana, Hartana Juliana Anggun Puspita Sari Krisna Rizki Fauzi Maulidi Kurnia, Adam Mochamad Kevin Sudrajat Muhammad Ammar Rasendriya Muhammad Kahfi Puspita Sari, Juliana Anggun Rasendriya, Muhammad Ammar Rasijah Amanda Sinaga Romi Satrio Wicaksono Sinaga, Rasijah Amanda Wijaya, Ahmad Reynaldi Zamzam Moch Priyamitra