Published: 2025-12-01
Classification Optimization of Aedes albopictus and Culex quinquefasciatus Mosquito Larvae Using Vision Transformer Method
DOI: 10.35870/ijsecs.v5i3.5120
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Abstract
Mosquito-transmitted diseases like Dengue Hemorrhagic Fever and Filariasis pose serious health threats throughout tropical regions, particularly in Indonesia. Quick and accurate identification of mosquito larvae plays a crucial role in disease prevention, especially for Aedes albopictus and Culex quinquefasciatus species that act as main disease carriers. Manual identification methods using microscopes or visual guides often struggle with time constraints, accuracy issues, and dependence on trained specialists. Our research focuses on improving the classification of Aedes albopictus and Culex quinquefasciatus mosquito larvae using Vision Transformer (ViT) technology, a deep learning method that has shown strong results in image recognition tasks. We applied the Vision Transformer model to classify mosquito larvae from microscopic field images. The study also tested how different factors impact model performance, such as image clarity, lighting conditions, and image resolution. Our findings show that using Vision Transformer in classification systems produced excellent results, achieving 98.00% accuracy in recall, precision, and F1-score measurements. The research reveals that Vision Transformer methods deliver better accuracy than traditional approaches like Convolutional Neural Networks and can be adapted into working systems for technology and healthcare sectors.
Keywords
Vision Transformer (ViT) ; Mosquito Larvae Classification ; Aedes Albopictus ; Culex Quinquefasciatus ; Deep Learning
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i3.5120
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Abdullah Al Faruq
Informatics Engineering Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Dadang Iskandar Mulyana
Informatics Engineering Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Sopan Adrianto
Informatics Engineering Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
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Akbar, F., & Mulyana, D. I. (2022). Optimasi klasifikasi jentik nyamuk pada citra digital menggunakan algoritma genetika dan augmentasi. Jurnal Widya, 3(2), 156-168. https://doi.org/10.54593/awl.v3i2.98
-
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Stephen, L., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022). https://doi.org/10.1109/ICCV48922.2021.00986
-
Akbar, A., & Mulyana, D. I. (2022). Optimasi klasifikasi batik betawi menggunakan data augmentasi dengan metode KNN dan GLCM. Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM), 3(2), 92-101. https://doi.org/10.31102/jatim.v3i2.1577
-
Nana, N., Mulyana, D. I., Akbar, A., & Zikri, M. (2022). Optimasi klasifikasi buah anggur menggunakan data augmentasi dan convolutional neural network. Smart Comp: Jurnalnya Orang Pintar Komputer, 11(2), 148-161. https://doi.org/10.30591/smartcomp.v11i2.3527
-
Mulyana, D. I., Hartanto, H., & Yel, M. B. (2022). Optimasi klasifikasi bunga kantong semar dengan menggunakan algoritma naïve bayes, data augmentasi dan PSO. Swabumi, 10(2), 167-174.
-
Ruswandi, M., Mulyana, D. I., & Awaludin, A. (2022). Optimasi klasifikasi kematangan buah alpukat menggunakan KNN dan fitur statistik. Smart Comp: Jurnalnya Orang Pintar Komputer, 11(2), 210-219. https://doi.org/10.30591/smartcomp.v11i2.3531
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