Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method
Abstract
Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows.
Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application.
Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification.
Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures.
Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.
References
[1] N. Vasković et al., “Morphological Characteristics of Skin Lesions in Cattle Naturally Infected with Lumpy Skin Disease Virus in Serbia,” Acta Vet. Brno., vol. 69, no. 4, pp. 369–378, Dec. 2019, doi: 10.2478/acve-2019-0031.
[2] I. Sendow, N. S. Assadah, A. Ratnawati, N. I. Dharmayanti, and M. Saepulloh, “Lumpy Skin Disease: Ancaman Penyakit Emerging Bagi Kesehatan Ternak Sapi Di Indonesia,” WARTAZOA., vol. 31, no. 2, pp. 85-96, Jun. 2021, doi: 10.14334/wartazoa.v31i2.2739.
[3] T. Sentoso, F. Ardiansyah, V. Tamuntuan, and S. Sastra Wangsa, “Sistemasi: Jurnal Sistem Informasi Identifikasi Lumpy Skin Disease pada Ternak Sapi dengan Klasifikasi Citra menggunakan Metode Convolutional Neural Network,” Sistemasi: Jurnal Sistem Informas., vol. 13, no. 3, pp. 864-873, May. 2024. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[4] N. S. Alfiansyah and Y. Litanianda, “Identifikasi Lumpy Skin Disease Menggunakan Tensorflow Dengan Metode Convolutional Neuron Network,” JATI (Jurnal Mahasiswa Teknik Informatika)., vol. 8, no. 4, pp. 7330-7336, Aug. 2024.
[5] A. Fikri, I. Aknuranda, and F. Pradana, “Pengembangan Sistem Informasi Aspirasi Online Berbasis Web Menggunakan Pemodelan Reuse-Oriented Development (Studi Kasus : DPM Universitas Brawijaya),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer., vol. 3, no. 2, pp. 1174-1183, Feb. 2019.
[6] S. Kumar and S. Shastri, “Lumpy Skin Images Dataset,” 2022, Mendeley Data. [Online]. Available: https://data.mendeley.com/datasets/w36hpf86j2/1
[7] Luqman Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379–385, Apr. 2021, doi: 10.29207/resti.v5i2.3001.
[8] D. B. Setyohadi and F. A. Kristiawan, “Preprocessing Iterative Partitioning Filter Alghorithm,” TELEMATIKA., vol. 14, no. 1, pp. 12-20, Apr. 2017.
[9] F. Alghifari and D. Juardi, “Fauzan Alghifari Penerapan Data Mining Pada Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes,” JURNAL ILMIAH INFORMATIKA (JIF)., vol. 9, no. 2, pp. 76-81, Sep. 2024
[10] A. Beikmohammadi, K. Faez, and A. Motallebi, “SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN,” Expert Syst. Appl., vol. 202, Sep. 2022, doi: 10.1016/j.eswa.2022.117470.
[11] J. Hang, D. Zhang, P. Chen, J. Zhang, and B. Wang, “Classification of plant leaf diseases based on improved convolutional neural network,” Sensors (Switzerland), vol. 19, no. 19, pp. 1-14, Oct. 2019, doi: 10.3390/s19194161.
[12] G. Kang, K. Liu, B. Hou, and N. Zhang, “3D multi-view convolutional neural networks for lung nodule classification,” PLoS One, vol. 12, no. 11, Nov. 2017, doi: 10.1371/journal.pone.0188290.
[13] W. Wang, Y. Hu, T. Zou, H. Liu, J. Wang, and X. Wang, “A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers,” Comput. Intell. Neurosci., vol. 2020, pp. 1-10, Aug. 2020, doi: 10.1155/2020/8817849.
[14] M. N. Winnarto, “Penerapan Arasitektur MobileNetV2 Pada Klasifikasi Penyakit Daun Teh,” Program Studi Ilmu Komputer (S2) Fakultas Teknologi Informasi Universitas Nusa Mandiri, Jakarta., Aug. 2021.
[15] Y. Miftahuddin and F. Zaelani, “Perbandingan Metode Efficientnet-B3 dan Mobilenet-V2 Untuk Identifikasi Jenis Buah-buahan Menggunakan Fitur Daun,” Jurnal Ilmiah Teknologi Informasi Terapan., vol. 9, no. 1, pp. 1-11, Dec. 2022.
[16] R. Yudha Adhitya, M. Khoirul Hasin, M. Syai, E. Setiawan, P. Studi Teknik Otomasi, and P. Perkapalan Negeri Surabaya, “Analisis Pengaruh Optimizer pada Model CNN untuk Identifikasi Cacat pada Perekat Kemasan,” JURNAL SISFOTENIKA., vol. 13, no. 2, Jul. 2023, doi: 10.30700/jst.v13i2.1447.
[17] A. Bagas Prakosa and D. Radius Tanone, “Implementasi Model Deep Learning Convolutional Neural Network (CNN) Pada Citra Penyakit Daun Jagung Untuk Klasifikasi Penyakit Tanaman,” Jurnal Pendidikan Teknologi Informasi (JUKANTI)., vol. 6, no. 1, pp. 107-116, Apr. 2023.
[18] Dinas Pertanian dan Ketahanan Pangan Provinsi Bali. (2022). "Waspada Penyakit Lumpy Skin Diseases (LSD)." [Online]. Available: https://distanpangan.baliprov.go.id/waspada-penyakit-lumpy-skin-diseases-lsd/. [Accessed: Jul. 25, 2024].
[19] P. T. L. Dew, F. Dewanta, and M. A. Nugroho, “Implementasi Machine Learning Model Deployment Pada Website Pemantauan Kondisi Sungai Citarum Menggunakan Platform-As-A-Service,” e-Proceeding of Engineering, vol. 8, no. 6, pp. 3064-3074, Dec. 2022.
[20] E. S. Bimo, “Wabah Lumpy Skin Disease Kian Merebak, Laos Perpanjang Larangan Impor Hewan Ternak Sapi.” [Online]. Available: https://www.kompas.tv/internasional/192365/wabah-lumpy-skin-disease-kian-merebak-laos-perpanjang-larangan-impor-hewan-ternak-sapi. [Accessed: Aug. 5, 2024].
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