Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Pengolahan Citra Digital untuk Mendukung Ketahanan Pangan Nasional
DOI:
https://doi.org/10.38035/jim.v4i6.1617Keywords:
Convolutional Neural Network, EfficientNetB0, penyakit daun padi, pengolahan citra digital, ketahanan panganAbstract
Penelitian ini mengkaji penyakit daun padi sebagai faktor utama yang menyebabkan penurunan hasil panen di Indonesia, di mana pemeriksaan manual oleh petani atau petugas lapangan sering berlangsung lambat, bersifat subjektif, dan rentan salah identifikasi. Kondisi ini menunjukkan perlunya sistem deteksi yang cepat, konsisten, dan akurat berbantuan teknologi digital. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit daun padi menggunakan Convolutional Neural Network dengan arsitektur EfficientNetB0 yang disesuaikan dalam kerangka pengolahan citra digital. Metode meliputi ekstraksi ciri otomatis, pembagian data secara proporsional menjadi kelompok pelatihan dan pengujian, serta optimasi model menggunakan prosedur komputasi. Kinerja model dinilai melalui akurasi, ketepatan, sensitivitas, skor F1, dan analisis matriks kebingungan. Hasil penelitian menunjukkan bahwa model mencapai tingkat akurasi tinggi pada kisaran 93–96 persen dengan performa yang stabil di seluruh kategori penyakit. Temuan ini menegaskan bahwa model mampu menangkap karakter visual kompleks penyakit daun padi dan memiliki potensi kuat untuk diintegrasikan dalam sistem deteksi dini otomatis. Sistem tersebut dapat meningkatkan ketepatan pemantauan penyakit dan mendukung ketahanan pangan nasional melalui pengurangan kehilangan hasil panen serta peningkatan kualitas pengambilan keputusan budidaya padi.
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