Rancang Bangun Sistem Pendeteksi Kebakaran Hutan Menggunakan Drone Berbasis Computer Vision

Abstract

The danger of forest and land fires in Indonesia is a serious threat that continues to recur
every year. According to U.S. data Fire Service, there are more than 700 forest fires every
year 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 and
fauna have fallen victim, increasing the risk of extinction. The use of UAVs can also change
disaster management. This technology can be integrated with early warning systems based
on analytical data and artificial intelligence, increasing the accuracy of fire predictions and
more proactive responses. Thus, the application of this technology is not just a temporary
solution, but a long-term strategic step to maintain environmental sustainability and
community welfare. Technological solutions such as UAVs and cooperation between
various parties are needed to overcome this challenge effectively. The aim of this research
is to create a computer vision-based system that uses deep learning algorithms, especially
You 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 from
spreading to other areas and reduce the damage caused by fires to humans and the
environment. 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 fire
intensity, time of day, and weather conditions, to ensure that the model can find fire
hotspots in various conditions.