| dc.description.abstract | Mosquitoes are small insects known as vectors of various infectious diseases that can harm humans. Indonesia has the second largest mosquito population in the world after Brazil with Aedes and Culex mosquito species being the most prevalent. These mosquito species can transmit various diseases such as dengue fever, malaria, chikungunya, and filariasis. The mosquito identification and classification process carried out to date often requires a long time and large resources. Therefore, a system is needed that can overcome these problems and help experts classify mosquitoes more easily and efficiently. This study uses mosquito body image data with a total dataset of 2,250 images, consisting of three mosquito species, namely Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. The total data is divided into 1,575 training data, 450 validation data, and 225 test data. The You Only Look Once version 7 (YOLOv7) algorithm is used in this research because it is generally able to detect objects accurately and has good performance. The test results show that the YOLOv7 algorithm is able to detect and classify three mosquito species well, achieving an accuracy of 95.1%. | en_US |
