SISTEM PENGENDALI KECEPATAN ADAPTIF BERBASIS NEURAL NETWORK PADA SISTEM KESELAMATAN MOBIL LISTRIK

RIVAI, MUHAMAD FAJAR (2026) SISTEM PENGENDALI KECEPATAN ADAPTIF BERBASIS NEURAL NETWORK PADA SISTEM KESELAMATAN MOBIL LISTRIK. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The rapid development of Electric Vehicle (EV)s has increased the need for safety systems capable of adaptively controlling vehicle speed according to the distance from objects ahead. Conventional speed control methods have limitations in automatically adjusting vehicle speed based on changing distances, which may reduce driving safety. Therefore, this study aims to design and implement an adaptive speed control system based on an Artificial Neural network (ANN) for a prototype Electric Vehicle (EV) using an ESP32 microcontroller. The proposed system employs an HC-SR04 ultrasonic sensor as the ANN input to detect object distance, while the ANN output generates Pulse Width Modulation (PWM)signals to control the DC motor speed through an L298N motor driver. An FC-03 encoder sensor is utilized to monitor the motor rotational speed (RPM), and all system parameters are transmitted to an Internet of Things (IoT)- based monitoring website using the WebSocket protocol for real-time visualization. The experimental results show that the HC-SR04 sensor achieved an average error of 1.46 cm (2.36%) with an accuracy of 97.64%. The ANN implementation on the ESP32 successfully generated adaptive PWM values ranging from 85 to 247, while the FC-03 encoder provided stable motor speed monitoring within the range of 240–729 RPM. These results demonstrate that the proposed adaptive speed control system was successfully implemented, and both the hardware and software operated properly according to the system design while providing reliable real-time monitoring. Keywords: Artificial Neural network (ANN), ESP32, Electric Vehicle (EV), Adaptive Speed Control, Internet of Things (IoT). Penelitian Perkembangan kendaraan listrik meningkatkan kebutuhan akan sistem yang mampu mengendalikan kecepatan secara adaptif sesuai kondisi di depan kendaraan. Pengendalian kecepatan secara konvensional memiliki keterbatasan dalam menyesuaikan perubahan jarak objek secara otomatis, sehingga diperlukan sistem yang mampu menjaga jarak aman selama kendaraan bergerak. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pengendali kecepatan adaptif berbasis Artificial Neural network (ANN) pada prototipe mobil listrik menggunakan mikrokontroler ESP32. Metode penelitian menggunakan sensor ultrasonik HC-SR04 sebagai masukan ANN untuk mendeteksi jarak objek, sedangkan keluaran ANN berupa nilai Pulse Width Modulation (PWM) digunakan untuk mengendalikan kecepatan motor DC melalui driver L298N. Sensor encoder FC-03 digunakan sebagai monitoring putaran motor (RPM), sementara seluruh data sistem dikirim ke website berbasis Internet of Things (IoT) menggunakan protokol WebSocket sehingga dapat dipantau secara real-time. Hasil pengujian menunjukkan bahwa sensor HC-SR04 memiliki rata-rata error sebesar 1,46 cm atau 2,36% dengan tingkat akurasi 97,64%. Implementasi ANN pada ESP32 mampu menghasilkan nilai PWM adaptif pada rentang 85–247, sedangkan sensor encoder FC-03 memantau putaran motor secara stabil pada rentang 240–729 RPM. Berdasarkan hasil tersebut, sistem pengendali kecepatan adaptif berhasil diimplementasikan dan seluruh komponen perangkat keras maupun perangkat lunak bekerja dengan baik sesuai perancangan, serta mampu melakukan monitoring data secara real-time. Kata Kunci : Artificial Neural network (ANN), ESP32, Mobil Listrik, Pengendali Kecepatan Adaptif, Internet of Things (IoT).

Item Type: Thesis (S1)
NIM/NIDN Creators: 41422010007
Uncontrolled Keywords: Artificial Neural network (ANN), ESP32, Mobil Listrik, Pengendali Kecepatan Adaptif, Internet of Things (IoT).
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan
600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.3 Electrical Engineering, Lighting, Superconductivity, Magnetic Engineering, Applied Optics, Paraphotic Technology, Electronics Communications Engineering, Computers/Teknik Elektro, Pencahayaan, Superkonduktivitas, Teknik Magnetik, Optik Terapan, Tekn
Divisions: Fakultas Teknik > Teknik Elektro
Depositing User: khalimah
Date Deposited: 24 Aug 2026 09:00
Last Modified: 24 Aug 2026 09:00
URI: http://repository.mercubuana.ac.id/id/eprint/103376

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