Identifikasi Penyakit Daun Jagung Menggunakan NASNet Mobile
DOI:
https://doi.org/10.29407/6vqytd91Keywords:
augmentasi data, citra daun jagung, NASNet Mobile, penyakit daun, deep learning.Abstract
Jagung merupakan komoditas pangan penting yang rentan terhadap serangan penyakit daun, seperti Common Rust, Gray Leaf Spot, dan Blight, yang dapat menurunkan hasil panen secara signifikan. Identifikasi penyakit secara manual masih bergantung pada pengamatan visual sehingga berpotensi menimbulkan kesalahan diagnosis. Oleh karena itu, penelitian ini bertujuan mengembangkan sistem identifikasi penyakit daun jagung berbasis citra menggunakan metode NASNet Mobile. Data penelitian terdiri dari citra daun jagung dengan empat kelas, yaitu Healthy, Common Rust, Gray Leaf Spot, dan Blight. Tahapan penelitian meliputi preprocessing citra berupa resize dan normalisasi, augmentasi data untuk meningkatkan variasi citra, serta pelatihan model menggunakan arsitektur NASNet Mobile dengan pendekatan transfer learning. Evaluasi kinerja model dilakukan menggunakan confusion matrix serta metrik akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mampu mencapai akurasi sebesar 91%, dengan performa terbaik pada kelas Healthy dan Common Rust, sementara kelas Gray Leaf Spot masih menunjukkan nilai recall yang lebih rendah akibat kemiripan visual antar penyakit. Hasil ini menunjukkan bahwa NASNet Mobile efektif digunakan untuk identifikasi penyakit daun jagung dan berpotensi dikembangkan sebagai sistem pendukung keputusan bagi petani dalam pengendalian penyakit tanaman secara lebih akurat.
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[1] B. Nurwenda, Saediman, and Yusria, “8550-8565,” Innov. J. Soc. Sci. Res., vol. 4, no. Analisis Dampak Perubahan Iklim Terhadap Ketahanan Pangan Petani Jagung Lokal Di Kecamatan Kabawo Kabupaten Muna, pp. 8550–8566, 2024.
[2] V. A. Puspita, U. A. Hamakonda, and P. Oba, “LOKAL DI KABUPATEN NGADA Program Studi Agroteknologi Sekolah Tinggi Pertanian Flores Bajawa , Ngada NTT * Email : ayupuspitav@gmail.com Abstrak INCREASING THE ECONOMIC VALUE OF CORN WITH FOOD DIVERSIFICATION OF LOCAL WISDOM-BASED PROCESSING IN NGADA DISTR”.
[3] M. Fiqriansyah et al., TEKNOLOGI BUDIDAYA TANAMAN JAGUNG ( Zea mays ) DAN SORGUM (Sorghum bicolor (L.) Moench). 2021.
[4] N. U. M. H. Lapui Ardiansyah, “Tepung Jagung 2,” J. Agropet, vol. 18, pp. 43–46, 2021, [Online]. Available: https://ojs.unsimar.ac.id/index.php/AgroPet/article/view/359
[5] F. Melia, F. M. Aldian, M. S. F. Pahlevi, R. N. I. Risqullah, and S. Oktaffiani, “Peran Pemerintah Dalam Meningkatkan Volume Ekspor Jagung,” J. Econ., vol. 2, no. 1, pp. 1305–1320, 2023, doi: 10.55681/economina.v2i1.287.
[6] H. Halimah, N. Hendri, and Syukrial, “Pengaruh Biaya Produksi dan Harga Jual Terhadap Pendapatan Petani Jagung Pada Petani Jagung Nagari Aie Tajun Kecamatan Lubuk Alung,” J. Point Equilib. Manaj. dan Akunt., vol. 6, no. 2, pp. 75–88, 2024, doi: 10.59963/jpema.v6i2.357.
[7] E. Ginting, T. S. Alasi, R. Alamsyah, M. Halim, and I. J. Tarigan, “Sistem Pakar Diagnosa Penyakit Tanaman Jagung Menggunakan Metode Forward Chaining Pada Kabupaten Langkat,” J. Armada Inform., vol. 8, no. 2, pp. 36–47, 2024, doi: 10.36520/jai.v8i2.130.
[8] A. Pfordt and S. Paulus, “A review on detection and differentiation of maize diseases and pests by imaging sensors,” J. Plant Dis. Prot., vol. 132, no. 1, pp. 1–21, 2025, doi: 10.1007/s41348-024-01019-4.
[9] G. T. Askale, A. B. Yibel, B. M. Taye, and G. D. Wubneh, “Mobile based deep CNN model for maize leaf disease detection and classification,” Plant Methods, vol. 21, no. 1, 2025, doi: 10.1186/s13007-025-01386-5.
[10] S. Kumar et al., “Spatial attention-guided pre-trained networks for accurate identification of crop diseases,” Sci. Rep., vol. 15, no. 1, pp. 1–29, 2025, doi: 10.1038/s41598-025-08004-3.
[11] H. Paul, H. Udayangani, K. Umesha, N. Lankasena, C. Liyanage, and K. Thambugala, “Maize leaf disease detection using convolutional neural network: a mobile application based on pre-trained VGG16 architecture,” New Zeal. J. Crop Hortic. Sci., vol. 53, no. 2, pp. 367–383, 2025.
[12] B. Widianto, E. Utami, and D. Ariatmanto, “Identifikasi Penyakit Tanaman Jagung Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” Techno.Com, vol. 22, no. 3, pp. 599–608, 2023, doi: 10.33633/tc.v22i3.8425.
[13] P. A. Azli, M. Minarni, A. Syahrani, G. Y. Swara, and A. Anisya, “Segmentasi Citra Daun Tomat untuk Klasifikasi Penyakit Tanaman Menggunakan Support Vector Machine (SVM),” J. Inform. J. Pengemb. IT, vol. 10, no. 4, pp. 997–1008, 2025, doi: 10.30591/jpit.v10i4.9404.
[14] Audy and Zaini, “Analisis Kualitas Jagung Berbasis IoT dengan Penerapan Model SSD Mobilenet dan Histogram,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 11, no. 2, pp. 79–87, 2022, doi: 10.22146/jnteti.v11i2.3434.
[15] D. Hindarto, “Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 8, no. 2, pp. 41–48, 2023, doi: 10.20527/jtiulm.v8i2.174.
[16] Z. Ma et al., “Maize leaf disease identification using deep transfer convolutional neural networks,” Int. J. Agric. Biol. Eng., vol. 15, no. 5, pp. 187–195, 2022, doi: 10.25165/j.ijabe.20221505.6658.
[17] W. Maximilliano and N. Rachmat, “Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification,” Brill. Res. Artif. Intell., vol. 5, no. 1, pp. 325–332, 2025, doi: 10.47709/brilliance.v5i1.6259.
[18] U. Vignesh and R. Elakya, “Identification of Unhealthy Leaves in Paddy by using Computer Vision based Deep Learning Model,” Int. J. Electr. Electron. Res., vol. 10, no. 4, pp. 796–800, 2022, doi: 10.37391/IJEER.100405.
[19] D. Iswantoro and D. Handayani UN, “Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode Convolutional Neural Network (CNN),” J. Ilm. Univ. Batanghari Jambi, vol. 22, no. 2, p. 900, 2022, doi: 10.33087/jiubj.v22i2.2065.
[20] O. V. Putra, M. Z. Mustaqim, and D. Muriatmoko, “Transfer Learning untuk Klasifikasi Penyakit dan Hama Padi Menggunakan MobileNetV2,” Techno.Com, vol. 22, no. 3, pp. 562–575, 2023, doi: 10.33633/tc.v22i3.8516.
[21] S. E. Nassar, I. Yasser, H. M. Amer, and M. A. Mohamed, “A robust MRI-based brain tumor classification via a hybrid deep learning technique,” J. Supercomput., vol. 80, no. 2, pp. 2403–2427, 2024, doi: 10.1007/s11227-023-05549-w.
[22] D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.
[23] N. Hardi, “Komparasi Algoritma MobileNet Dan Nasnet Mobile Pada Klasifikasi Penyakit Daun Teh,” Reputasi J. Rekayasa Perangkat Lunak, vol. 3, no. 1, pp. 50–55, 2022, doi: 10.31294/reputasi.v3i1.1313.
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