Akurasi Pendeteksian Berdasarkan Parameter Jarak, Jumlah Obyek dan Kekeruhan Air pada Obyek Bergerak Jentik Nyamuk

Aan Febriansyah; Surojo Surojo; Rafif Tri  Pangestu; Savira Karimah

doi:10.33504/manutech.v16i02.396

Authors

Aan Febriansyah Politeknik Manufaktur Negeri Bangka Belitung
Surojo Surojo
Rafif Tri Pangestu
Savira Karimah

DOI:

https://doi.org/10.33504/manutech.v16i02.396

Keywords:

Mosquito Larvae, Deep Learning, Detection, Training

Abstract

A system for detecting the presence of mosquito larvae can be used as a solution to find out whether the air reservoir is healthy or not. This mosquito larva detection system uses several measuring parameters, including the distance between the object of the mosquito larva and the camera, air turbidity/light conditions, and the number of mosquito larvae in the air protector. From the results of tests carried out on the detection distance parameters, the results showed that the system could detect the presence of mosquito larvae within a distance of 5-15 cm in clear air/bright light conditions with a detection success rate above 80%. In the same test, but with cloudy air/dark light conditions, detection went well but a detection error occurred at a distance of 15 cm, namely the system detected objects other than mosquito larvae as mosquito larvae. This is due to several factors, including the lack of variation in the dataset, the detection system is carried out in real-time (in the form of video capture) and the camera specifications used do not meet the minimum value required by the computing system.

Downloads

Download data is not yet available.

Author Biography

Aan Febriansyah, Politeknik Manufaktur Negeri Bangka Belitung

References

H. Harfriani, "Efektivitas Larvasida Ekstrak Daun Sirsak Dalam Membunuh Jentik Nyamuk," Jurnal Kesehatan Masyaraka, vol. 7, no. 2, pp. 164-169, 2012.

K. A. Sugeng Hariyadi, "Pemantauan Jentik Nyamuk Demam Berdarah Dengue Dalam Pandangan Medis dan Retorika Syariat (Potret KKN di Desa Bangetayu Kulon Kota Semarang)," BudAI: Multidisciplinary Journal of Islamic Studies, vol. 1, no. 2, pp. 79-87, 2022.

R. Ardina, Nurhalina, Suratno, D. Purbayanti, F. Sartika and Agus, "Survei Jentik Nyamuk Aedes Aegypti Di Perumahan Wilayah Kerja Puskesmas Menteng Kota Palangka Raya," PengabdianMu, vol. 3, no. 1, pp. 55-61, 2018.

R. T. Pangestu, S. Karimah, A. Febriansyah and Irwan, "Implementasi Metode YOLOv5 Pada Sistem Pendeteksi Jentik Nyamuk Berbasis IoT," Jurnal Inovasi Teknologi Terapan, vol. 2, no. 2, 2024.

F. Akbar and M. D. Iskandar, "Optimasi Klasifikasi Jentik Nyamuk Pada Citra Digital Menggunakan Algoritma Genetika Dan Augmentasi," Jurnal Widya, vol. 3, no. 2, pp. 156-168, 2022.

M. Z. H. Mulyawan, "Identifikasi dan Tracking Objek Berbasis Image Processing Secara Real Time," Jurnal Telekomunikasi Poleteknik Elektronika Negeri Surabaya, p. 1–15, 2020.

M. Febian Fitra and R. Naim, "Klasifikasi Citra Buah Menggunakan Convolutional Neural Network," Journal of Informatics and Computer Science (JINACS), vol. 1, no. 2, pp. 104-108, 2019.

S. A. Asmai et al., "Mosquito Larvae Detection using Deep Learning," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 12, p. 804–809, 2019.

A. A. Jalife et al., "Aedes Mosquito Detection In Its Larval Stage Using Deep Neural Networks," Elsevier B.V., vol. 189, 2020.

Z. A. Muhammad Izzul Azri Bin and S. Aliza Binti, "Aedes Larvae Classification and Detection (ALCD) System by Using Deep Learning," in Institute of Electrical and Electronics Engineers Inc., Malaysia, 2020.

A. Surya, D. B. Peral, A. VanLoon and A. Rajesh, "A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae Classification," in arXiv:2209.07718, United States, 2022.

M. S. Saeed, S. F. Nazreen, S. S. S. A. Ullah, Z. F. Rinku and M. A. Rahman, "Detection of Mosquito Larvae Using Convolutional Neural Network," in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), DHAKA, Bangladesh, 2021.

G. E. Negara, A. A. Harnawan, S. N. Listyaputra and A. Pribadi, "Co-Jec (Counting Object) Jentik Nyamuk Aedes Aegypti menggunakan Metode Pengolahan Citra Digital," Jurnal Fisika FLux, vol. 1, no. 1, pp. 120-127, 2019.

D. B. M. Yuana, W. T. Sesulihatien, A. Basuki, T. Harsono, A. Alimudin and E. A. Rohmah, "Mobile sensing in aedes aegypti larva detection with biological feature extraction," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1454-1460, 2020.

M. A. M. Fuad, M. R. A. Ghani, R. Ghazali, T. A. Izzuddin, M. F. Sulaima, Z. Jano and T. Sutikno, "Detection of Aedes aegypti larvae using single shot multibox detector with transfer learning," Bulletin of Electrical Engineering and Informatics (BEEI), vol. 8, no. 2, pp. 514-518, 2019.