Implementasi Metode YOLOv5 pada Sistem Pendeteksi Jentik Nyamuk Berbasis IoT
DOI:
https://doi.org/10.33504/jitt.v2i2.184Keywords:
Mosquito Larvae, Mosquito Larvae Detector, You Only Look Once (YOLO)Abstract
Indonesia is a tropical country that generally faces the risk of widespread mosquito distribution in each of its regions. With the abudance of mosquito distribution, the spread of mosquito larvae will also increase. As a result, mosquito larvae fins suitable places to breed in hard-to-reach areas. Therefore, a tool is needed to monitor these mosquito larvae when they are in water reservoir or containers that are difficult to access. The method used in this final project is You Only Look Once (YOLO). Based on the system can perform detection but is not yet working optimally. The system can detect well in places with bright light intensity or not too dark. The test result of this system show that it can detect many mosquito larvae at once. The accuracy results obtained from testing range from 51% - 89%.
Downloads
References
M. I. A. B. Z. Azman and A. B. Sarlan, “Aedes Larvae Classification and Detection (ALCD) System by Using Deep Learning,” in 2020 International Conference on Computational Intelligence, ICCI 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 179–184. doi: 10.1109/ICCI51257.2020.9247647.
M. S. Saeed, S. F. Nazreen, S. S. S. A. Ullah, Z. F. Rinku, and A. Rahman, “Detection of Mosquito Larvae Using Convolutional Neural Network,” in International Conference on Robotics, Electrical and Signal Processing Techniques, 2021, pp. 478–482. doi: 10.1109/ICREST51555.2021.9331235
M. A. M. Fuad et al., “Detection of Aedes aegypti larvae using single shot multibox detector with transfer learning,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 2, pp. 514–518, 2019.
M. Z. Hendy Mulyawan, “Identifikasi dan tracking objek berbasis image processing secara real time,” Jurnal Telekomunikasi Poleteknik Elektronika Negeri Surabaya, pp. 1–15, 2020.
F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” Journal of Informatics and Computer Science (JINACS), vol. 1, no. 02, pp. 104–108, 2019.
M. H. Diponegoro, S. S. Kusumawardani, and I. Hidayah, “Tinjauan pustaka sistematis: implementasi metode deep learning pada prediksi kinerja murid,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi| Vol, vol. 10, no. 2, 2021.
P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (Cnn) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–20, 2020.
W. S. E. Putra, “Klasifikasi citra menggunakan convolutional neural network (CNN) pada caltech 101,” Jurnal Teknik ITS, vol. 5, no. 1, 2016.
K. Khairunnas, E. M. Yuniarno, and A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” Jurnal Teknik ITS, vol. 10, no. 1, pp. A50–A55, 2021.
H. Kusumah, M. S. Zahran, K. N. Rifqi, D. Alawiyah, and E. M. W. H. Putri, “Deep Learning Pada Detektor Jerawat: Model YOLOv5,” Journal Sensi Online ISSN, vol. 2655, p. 5298.
Redha Devan Naratama, “Studi Komparasi Performa Algoritma Deteksi Objek pada Raspberry PI,” Magelang, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jurnal Inovasi Teknologi Terapan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.