Prototipe lampu lalu lintas menggunakan PLC dan SCADA berbasis computer vision dengan raspberry pi 4B

Anton Firmansyah; Andri Suyadi; Alif Akram Khalish; Al Farick Zulhanudin; Syafrudin Syafrudin

doi:10.33795/eltek.v23i1.6380

Authors

Anton Firmansyah Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
Andri Suyadi Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
Alif Akram Khalish Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
Al Farick Zulhanudin Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
Syafrudin Syafrudin Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.33795/eltek.v23i1.6380

Keywords:

Machine Learning, PLC, Raspberry Pi 4b, Smart Traffic Light, SCADA

Abstract

Pertumbuhan populasi dan peningkatan jumlah kendaraan di wilayah perkotaan telah menimbulkan tantangan serius dalam manajemen lalu lintas, terutama di persimpangan yang sering mengalami kemacetan. Sistem lampu lalu lintas konvensional yang tidak mampu merespons kondisi lalu lintas secara real-time menyebabkan pengaturan durasi lampu yang tidak efisien, memperburuk kemacetan, meningkatkan emisi karbon, serta menyebabkan pemborosan bahan bakar. Seiring dengan perkembangan teknologi, machine learning digunakan untuk mengoptimalkan pengaturan lalu lintas secara adaptif. Dalam penelitian ini, dikembangkan sistem lampu lalu lintas cerdas berbasis Programmable Logic Controller (PLC) yang dikombinasikan dengan Raspberry Pi 4B sebagai pusat pemrosesan data. Sistem ini juga terintegrasi dengan Smart Traffic Light, yang memungkinkan pengaturan durasi lampu berdasarkan analisis data lalu lintas secara real-time. Selain itu, penerapan Supervisory Control and Data Acquisition (SCADA) memungkinkan pemantauan dan pengendalian sistem secara efisien melalui antarmuka pengguna. Dengan memanfaatkan teknologi ini, sistem diharapkan dapat meningkatkan efisiensi lalu lintas dan mengurangi kemacetan dengan penyesuaian otomatis berbasis data.

ABSTRACT

The growth of population and the increasing number of vehicles in urban areas have posed significant challenges in traffic management, especially at intersections that frequently experience congestion. Conventional traffic light systems that cannot respond to real-time traffic conditions result in inefficient timing adjustments, exacerbating congestion, increasing carbon emissions, and causing fuel wastage. With technological advancements, machine learning is utilized to optimize traffic control adaptively. This study develops an intelligent traffic light system based on a Programmable Logic Controller (PLC), integrated with a Raspberry Pi 4B as the data processing center. The system is also incorporated with Smart Traffic Light technology, enabling adaptive light duration adjustments based on real-time traffic data analysis. Additionally, the implementation of Supervisory Control and Data Acquisition (SCADA) allows efficient system monitoring and control through a user interface. By leveraging these technologies, the system is expected to enhance traffic efficiency and reduce congestion through automated data-driven adjustments.

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