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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Usman, Farizal Justian
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Computer Science & IT

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Rancang Bangun Sistem Pendeteksi Kebakaran Hutan Menggunakan Drone Berbasis Computer Vision Usman, Farizal Justian; Suwarno, Muhammad Anno
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 2 (2025): IKRAITH-INFORMATIKA Vol 9 No 2 Juli 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v9i2.4386

Abstract

The danger of forest and land fires in Indonesia is a serious threat that continues to recurevery year. According to U.S. data Fire Service, there are more than 700 forest fires everyyear burning more than 7 million hectares of land. These fires not only damage ecosystems,but also cause the loss of important biodiversity. Many of Indonesia's endemic flora andfauna have fallen victim, increasing the risk of extinction. The use of UAVs can also changedisaster management. This technology can be integrated with early warning systems basedon analytical data and artificial intelligence, increasing the accuracy of fire predictions andmore proactive responses. Thus, the application of this technology is not just a temporarysolution, but a long-term strategic step to maintain environmental sustainability andcommunity welfare. Technological solutions such as UAVs and cooperation betweenvarious parties are needed to overcome this challenge effectively. The aim of this researchis to create a computer vision-based system that uses deep learning algorithms, especiallyYou Only Look Once (YOLO), to detect forest fires quickly and accurately. In mitigating forest fire disasters, early detection of hotspots is an important step to prevent fires fromspreading to other areas and reduce the damage caused by fires to humans and theenvironment. To achieve the objectives of this study, several actions were taken. First,forest fire image data is collected and processed for use in training the YOLO model. Next,the model is trained using a dataset that covers various forest fire conditions, such as fireintensity, time of day, and weather conditions, to ensure that the model can find firehotspots in various conditions.
Co-Authors Suwarno, Muhammad Anno