Android Based Preliminary Diagnosis of Foot and Mouth Disease in Local Cattle Using Forward Chaining and MobileNetV2
DOI:
https://doi.org/10.33795/jartel.v16i3.10923Keywords:
Android application, convolutional neural network, expert system, Foot and Mouth Disease, Forward Chaining, MobileNetV2Abstract
Foot and Mouth Disease is a highly contagious livestock disease that requires rapid preliminary assessment before veterinary confirmation. This study developed an Android application that combines a Forward Chaining expert system with MobileNetV2 image classification for early assessment of local cattle. The expert system processes seven observable symptoms, whereas the image module classifies mouth and hoof images into four categories: healthy mouth, healthy hoof, diseased mouth, and diseased hoof. The final experiment used 8212 training images, 1528 validation images, and 1278 testing images. The trained model was converted to a five megabyte TensorFlow Lite file for on device inference. The reported overall image classification accuracy was 91.86 percent, with macro precision, recall, and F1 score of 88.77 percent, 89.50 percent, and 89.13 percent. The Forward Chaining module correctly classified 14 of 15 cases, producing 93.3 percent accuracy. All eighteen tested farmer and veterinarian functions operated successfully on Android devices, and nine users produced an average usability score of 89.78 percent. The integrated application can support faster field screening while retaining veterinary consultation as the final clinical authority.
References
```text
H. Shahab, M. Iqbal, A. Sohaib, A. ur Rehman, A. Bermak, and K. Munir, “Design and implementation of an IoT based monitoring system for early detection of lumpy skin disease in cattle,” Smart Agricultural Technology, vol. 9, p. 100609, 2024, doi: 10.1016/j.atech.2024.100609.
N. Longjam, R. Deb, A. K. Sarmah, T. Tayo, V. B. Awachat, and V. K. Saxena, “A brief review on diagnosis of Foot and Mouth Disease of livestock: Conventional to molecular tools,” Veterinary Medicine International, vol. 2011, p. 905768, 2011, doi: 10.4061/2011/905768.
A. U. Bani and A. Asruddin, “Pendeteksian Penyakit Mulut dan Kuku pada sapi dengan menerapkan metode Naive Bayes,” Journal of Computer System and Informatics, vol. 3, no. 4, pp. 264-268, 2022, doi: 10.47065/josyc.v3i4.1934.
M. R. Zamroni, Q. C. K. N. S., and A. Wahyudi, “Sistem pakar diagnosa penyakit sapi sebagai upaya pencegahan penyebaran wabah PMK di Lamongan,” Jurnal Ilmiah Informatika, vol. 10, no. 2, pp. 145-152, 2022, doi: 10.33884/jif.v10i02.6373.
M. A. S. Padilla Savira and Novriyenni, “Diagnosis of cattle disease as an effort to prevent the spread of FMD using the Dempster Shafer method in Langkat district,” Journal of Information Technology, vol. 2, pp. 85-94, 2023.
I. W. Prastika and E. Zuliarso, “Deteksi penyakit kulit wajah menggunakan TensorFlow dengan metode Convolutional Neural Network,” Jurnal Manajemen Informatika dan Sistem Informasi, vol. 4, no. 2, pp. 84-91, 2021, doi: 10.36595/misi.v4i2.418.
A. J. Rozaqi, A. Sunyoto, and M. R. Arief, “Deteksi penyakit pada daun kentang menggunakan pengolahan citra dengan metode Convolutional Neural Network,” Creative Information Technology Journal, vol. 8, no. 1, pp. 22-31, 2021, doi: 10.24076/citec.2021v8i1.263.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018, doi: 10.1109/CVPR.2018.00474.
M. Sari, S. Defit, and G. W. Nurcahyo, “Sistem pakar deteksi penyakit pada anak menggunakan metode Forward Chaining,” Jurnal Sistim Informasi dan Teknologi, vol. 2, pp. 130-135, 2020, doi: 10.37034/jsisfotek.v2i4.34.
A. Widyadhari, C. Basri, and E. Sudarnika, “High Risk Period kasus Penyakit Mulut dan Kuku pasca wabah pada peternakan sapi perah di Kabupaten Malang,” Jurnal Sain Veteriner, vol. 42, no. 3, 2024, doi: 10.22146/jsv.91219.
M. I. Nepesov, D. Kilinc, and R. G. Sezer Yamanel, “Comparison of infrared smart mobile phone thermometer to non contact forehead infrared thermometer in pediatric emergency room patients,” Iranian Journal of Pediatrics, vol. 34, no. 5, 2024, doi: 10.5812/ijp-144744.
M. A. Al Hawari Nasution, S. Siswanto, and E. Suryana, “Rancangan media pembelajaran berupa aplikasi Augmented Reality berbasis Android,” Jurnal Media Infotama, vol. 19, no. 2, pp. 528-537, 2023, doi: 10.37676/jmi.v19i2.4771.
Y. N. Yenusi, S. Trihandaru, and A. Setiawan, “Comparison of Convolutional Neural Network models in face classification of Papuan and other ethnicities,” Jurnal Sains dan Teknologi, vol. 12, no. 1, pp. 261-268, 2023, doi: 10.23887/jstundiksha.v12i1.46861.
M. Ihsan, R. K. Niswatin, and D. Swanjaya, “Deteksi ekspresi wajah menggunakan TensorFlow,” Joutica, vol. 6, no. 1, p. 428, 2021, doi: 10.30736/jti.v6i1.554.
A. Fiorenza and H. Tolle, “Pengembangan aplikasi mobile sebagai media edukasi kesehatan gigi menggunakan teknologi Firebase serta metode prototyping,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 1, pp. 258-266, 2023.
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