Penerapan Computer Vision Pada Unmanned Aerial Vehicle (Uav) Untuk Deteksi Titik Api.

Aliffian, Anfasa (2022) Penerapan Computer Vision Pada Unmanned Aerial Vehicle (Uav) Untuk Deteksi Titik Api. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Menurut The State of Indonesia’s Forest 2020 yang diterbitkan oleh Kementrian Lingkungan Hidup dan Kehutanan, luas area hutan di Indonesia mencapai 120,3 Juta Hektar. Sementara itu, kebakaran hutan dan lahan yang terjadi di Indonesia pada periode Januari hingga November 2021 mencapai angka 353.222 Hektar, meningkat sebesar 15,93% dibandingkan dengan tahun 2020. Penelitian dilakukan untuk membuat implementasi sistem pendeteksi api menggunakan Unmanned Aerial Vehicle (UAV). Penelitian dilakukan menggunakan mikrokontroler Raspberry Pi 4 sebagai perantara komunikasi antara perangkat ponsel pintar dengan drone Parrot ANAFI, koneksi perangkat dilakukan menggunakan Bluetooth dan Wi-Fi. Program deteksi api diimplementasikan pada Raspberry Pi 4 menggunakan computer vision, deteksi api diolah berdasarkan citra yang ditangkap oleh kamera drone Parrot ANAFI. Pergerakan drone Parrot ANAFI diprogram menggunakan Software Development Kit (SDK) Olympe yang telah disediakan oleh Parrot. Hasil penelitian menunjukan bahwa implementasi computer vision pada unmanned aerial vehicle (UAV) untuk deteksi titik api berhasil dilakukan. Hasil penelitian menunjukan bahwa uji coba deteksi api pada waktu yang berbeda (pagi, siang, dan malam) dan pada ketinggian yang berbeda (1 meter sampai 5 meter) dapat dilakukan dengan baik, presentase keberhasilan dari uji coba adalah 100% dari 5 kali percobaan.
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According to The State of Indonesia's Forest 2020 published by the Ministry of Environment and Forestry, the total forest area in Indonesia reaches 120.3 million hectares. Meanwhile, forest and land fires that occurred in Indonesia in the period January to November 2021 reached 353,222 hectares, an increase of 15.93% compared to 2020. The research was conducted to implement a fire detection system using an Unmanned Aerial Vehicle (UAV). The study was conducted using a Raspberry Pi 4 microcontroller as a communication intermediary between smartphone devices and Parrot ANAFI drones, device connections were made using Bluetooth and Wi-Fi. The fire detection program is implemented on the Raspberry Pi 4 using computer vision, fire detection is processed based on the image captured by the Parrot ANAFI drone camera. The movement of the Parrot ANAFI drone is programmed using the Olympe Software Development Kit (SDK) provided by Parrot. The results showed that the implementation of computer vision on an unmanned aerial vehicle (UAV) for hotspot detection was successful. The results showed that fire detection trials at different times (morning, afternoon, and night) and at different heights (1 meter to 5 meters) was successful, the percentage of success of the trial was 100% out of 5 trials.

Item Type:	Thesis (Other)
Additional Information:	RSIf 006.37 Ali p-1 2022
Uncontrolled Keywords:	Raspberry Pi 4 Model B, Drone, Computer Vision, Parrot ANAFI. Raspberry Pi 4 Model B, Drone, Computer Vision, Parrot ANAFI.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	26 May 2026 06:40
Last Modified:	26 May 2026 06:40
URI:	http://repository.its.ac.id/id/eprint/133453

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