<![CDATA[To reduce losses due to fire, it is necessary to extinguish and rescue immediately. However, in the dense area fire trucks were unable to reach the fire site due to narrow road access. In this case, drones that can fly by themselves to the point of fire then release fire-fighting bombs automatically can help fire disaster management. This means it needs a system where it can identify whether there is a fire. This study explores the idea of identifying fire using computer vision approach by making 8 identification models with each dataset of day, night, day, and night, thermal, day filter, night filter, day and night filter, and thermal filter, which had been tested by a set of data that corresponded to each dataset. YOLOv4 algorithm and Google Colaboratory were used, where each model took 8-10 hours to be trained. Results show that the day and night model was the most robust by having the highest average F1-score, 0.37. And will be performing the best on thermal data test with the value of F1-score is 0.6. This can be a representation for exploring new ideas on further study of how to obtain the most suitable dataset and data test.]]>
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