Deteksi Dini Penyakit Tanaman Jagung Berbasis Transfer Learning dengan Arsitektur DenseNet121
DOI:
https://doi.org/10.30865/json.v7i2.9094Keywords:
Transfer learning; DenseNet121; Klasifikasi citra; Penyakit daun jagung; Pertanian presisiAbstract
Jagung (Zea mays L.) merupakan komoditas pangan strategis di Indonesia yang berperan penting dalam ketahanan pangan, industri pakan ternak, hingga energi terbarukan. Produktivitas jagung kerap menurun akibat penyakit daun seperti Blight, Common Rust, dan Gray Leaf Spot, yang dapat mengurangi hasil panen hingga 30–50% jika tidak dideteksi sejak dini. Metode deteksi konvensional melalui pengamatan visual masih memiliki keterbatasan, antara lain subjektivitas penilaian, kurangnya tenaga ahli, serta keterlambatan dalam pengambilan keputusan. Oleh karena itu, diperlukan pendekatan berbasis kecerdasan buatan yang mampu melakukan deteksi secara cepat, akurat, dan efisien di lapangan. Penelitian ini mengusulkan penggunaan transfer learning dengan arsitektur DenseNet121 untuk klasifikasi penyakit daun jagung. Dataset yang digunakan terdiri dari 2.818 citra yang terbagi ke dalam empat kelas utama (Blight, Common Rust, Gray Leaf Spot, Healthy), diperoleh dari kombinasi dokumentasi lapangan dan dataset terbuka daring. Data kemudian dibagi menggunakan stratified split menjadi 68% latih, 17% validasi, dan 15% uji. Hasil pengujian menunjukkan bahwa model mencapai akurasi 93,48% dengan F1-score rata-rata 0,93. Kelas Healthy dan Common Rust teridentifikasi hampir sempurna, sementara kesalahan klasifikasi masih ditemukan pada Gray Leaf Spot yang sering terprediksi sebagai Blight. Kurva akurasi dan loss memperlihatkan dinamika pelatihan yang stabil tanpa indikasi overfitting, berkat penerapan augmentasi data, dropout, dan early stopping. Temuan ini menegaskan bahwa DenseNet121 berpotensi besar untuk diterapkan dalam sistem deteksi dini penyakit jagung berbasis AI, sekaligus mendukung pengembangan pertanian presisi dan peningkatan produktivitas nasional.
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