OPTIMISASI MODEL CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT LUMPY SKIN PADA SAPI
Abstract
Lumpy Skin Disease is a disease in cattle that causes decreased productivity and economic losses for farmers. Between July 2023 and June 2024, 6,803 cases were recorded in Indonesia, having a significant impact on the livestock industry. Early detection is crucial for controlling the spread of this disease. This study aims to optimize the Convolutional Neural Network for Lumpy Skin Disease classification by comparing the performance of several architectures, DenseNet-121, MobileNetV1, MobileNetV2, and GoogleNet. The dataset used was from Kaggle and consisted of 1,356 images. Results showed that GoogleNet achieved the best performance, with an Accuracy of 85.97% without segmentation and increasing to 87.03% with segmentation. However, segmentation does not continually improve Accuracy, as evidenced by DenseNet-121 and MobileNetV1, which experienced a slight decrease in Accuracy. In contrast, MobileNetV2 increased from 82.65% to 84.50%. This result shows that GoogleNet is more reliable in distinguishing lumpy skin and normal skin images than other architectures.
Lumpy Skin Disease adalah penyakit viral pada sapi yang menyebabkan penurunan produktivitas dan kerugian ekonomi bagi peternak. Sejak Juli 2023 hingga Juni 2024, tercatat 6.803 kasus di Indonesia, yang berdampak signifikan pada industri peternakan. Deteksi dini sangat penting untuk mengendalikan penyebaran penyakit ini. Penelitian ini bertujuan mengoptimalkan Convolutional Neural Network untuk klasifikasi Lumpy Skin Disease dengan membandingkan performa beberapa arsitektur, yaitu DenseNet-121, MobileNetV1, MobileNetV2, dan GoogleNet. Dataset yang digunakan diambil dari Kaggle dan terdiri dari 1.356 citra,. Hasil penelitian menunjukkan bahwa GoogleNet memiliki performa terbaik dengan akurasi 85,97% tanpa segmentasi dan meningkat menjadi 87,03% dengan segmentasi. Namun, segmentasi tidak selalu meningkatkan akurasi, terbukti pada DenseNet-121 dan MobileNetV1 yang mengalami sedikit penurunan. Sebaliknya, MobileNetV2 mengalami peningkatan dari 82,65% menjadi 84,50%. Hal ini menunjukkan bahwa GoogleNet lebih andal dalam membedakan citra lumpy skin dan normal dibandingkan arsitektur lainnya.
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Abutarbush, S. M. (2017). Lumpy Skin Disease (Knopvelsiekte, Pseudo-Urticaria, Neethling Virus Disease, Exanthema Nodularis Bovis). In Emerging and Re-emerging Infectious Diseases of Livestock (pp. 309–326). Cham, Switzerland : Springer. https://doi.org/10.1007/978-3-319-47426-7_14
Ainur Rahman, & Suroyo, H. (2021). Analisis Data Produk Elektronik Di E-Commerce Dengan Metode Algoritma K-Means Menggunakan Python. Journal of Advances in Information and Industrial Technology, 3(2), 11–18. https://doi.org/10.52435/jaiit.v3i2.158
Alfiansyah, N. S., & Litanianda, Y. (2024). Identifikasi Lumpy Skin Disease Menggunakan Tensorflow Dengan Metode Convolutional Neuron Network. JATI : Jurnal Mahasiswa Teknik Informatika, 8(4), 7330–7336. https://doi.org/10.36040/jati.v8i4.10238
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 53. https://doi.org/10.1186/s40537-021-00444-8
Febriyanti, F. A. (2024). Image Processing Dengan Metode Convolutional Neural Network (Cnn) Untuk Deteksi Penyakit Kulit Pada Manusia. Kohesi: Jurnal Sains Dan Teknologi, 3(10), 21–30. https://doi.org/10.3785/kohesi.v3i10.4088
Gupta, T., Patial, V., Bali, D., Angaria, S., Sharma, M., & Chahota, R. (2020). A review: Lumpy Skin Disease and its emergence in India. Veterinary Research Communications, 44, 111–118. https://doi.org/10.1007/s11259-020-09780-1
Li, H., Zhang, X., Liu, Y., Zhang, Y., Wang, Q., Zhou, X., Liu, J., Wu, H., & Wang, H. (2019). D-net: A simple framework for improving the generalization of machine reading comprehension. MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering, 212–219.
Martins, D., & Diesel, L. (2024). Developing a Convolutional Neural Network (CNN) Model for Facial Expression Recognition (FER). https://doi.org/10.63227/956.493.45
Musfiroh, D., Khaira, U., Utomo, P. E. P., & Suratno, T. (2021). Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 1(1), 24–33. https://doi.org/10.57152/malcom.v1i1.20
Namazi, F., & Tafti, A. K. (2021). Lumpy Skin Disease, an emerging transboundary viral disease: A review. Veterinary Medicine and Science, 7(3), 888–896. https://doi.org/10.1002/vms3.434
Putri, R. A., Satyawan, A. S., Prihantono, J. A., Linggi, R. S., Paramita, I. G. A. P. S., Iswarawati, N. K. E., Akbar, F., & Utomo, P. A. (2024). Model Deep Learning untuk Klasifikasi Objek pada Gambar Fisheye. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(3), 519–528. https://doi.org/10.25126/jtiik.938047
Raharjo, B. (2022). Deep Learning dengan Python. Semarang : Yayasan Prima Agus Teknik.
Ratniasih, N. L., Jayanti, N. W. N., & ... (2023). Klasifikasi Kepuasan Mahasiswa Menggunakan Algoritma Support Vector Machine dan Metode Stemming Sastrawi. Prosiding CORISINDO, 373–378.
Santoso, D. P., & Wibowo, W. (2022). Analisis Sentimen Ulasan Aplikasi Buzzbreak Menggunakan Metode Naïve Bayes Classifier pada Situs Google Play Store. Jurnal Sains Dan Seni ITS, 11(2). https://doi.org/10.12962/j23373520.v11i2.72534
Sentoso, T., Ardiansyah, F., Tamuntuan, V., Wangsa, S. S., Kusrini, & Kusnawi. (2025). Identification of Lumpy Skin Disease in Cattle with Image Classification using the Convolutional Neural Network Method. Sistemasi: Jurnal Sistem Informasi, 14(5), 864–873. https://doi.org/10.32520/stmsi.v13i3.2569
Susanto, R. I., & Tinaliah. (2024). Klasifikasi Penyakit Cacar Menggunakan Arsitektur AlexNet. Jurnal Algoritme, 5(1), 47–56. https://doi.org/10.35957/algoritme.v5i1.9045
DOI: https://doi.org/10.56486/jeis.vol5no2.783
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