Penyakit tanaman merupakan ancaman utama terhadap ketahanan
pangan global, dengan Food and Agriculture Organization (FAO)
memperkirakan kerugian hasil panen tahunan hingga 40% di seluruh
dunia. Deteksi dini dan akurat terhadap penyakit-penyakit ini
sangat penting untuk memungkinkan intervensi tepat waktu dan
meminimalkan kerugian pertanian. Penelitian ini mengusulkan
pendekatan berbasis deep learning untuk klasifikasi penyakit daun
tanaman secara otomatis menggunakan arsitektur ResNet-50 yang
dikombinasikan dengan transfer learning. Model dilatih dan
dievaluasi pada dataset PlantVillage yang berisi 54.305 gambar
berlabel daun tanaman sehat dan sakit mencakup 38 kelas dari 14
spesies tanaman. Model berhasil mencapai akurasi pengujian
Article History
Received: November 2025
Reviewed: November 2025
Published: November 2025
Plagiarism Checker No
235
Prefix DOI :
10.8734/Kohesi.v1i2.365
Copyright : Author
Publish by : Kohesi
This work is licensed
under a Creative
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NonCommercial 4.0
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E-ISSN : 2988-1986 https://ejournal.cahayailmubangsa.institute/index.php/kohesi Kohesi: Jurnal Multidisiplin Saintek
Volume 10 No 6 Tahun 2025
keseluruhan sebesar 96,32%, dengan presisi, recall, dan F1-score
rata-rata makro masing-masing sebesar 95,87%, 95,64%, dan
95,75%.
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