Deteksi Hama Penyakit Daun Padi Dengan Menggunakan Teknik Optimasi Deep Learning Convolutional Neural Network
DOI:
https://doi.org/10.32877/bt.v7i3.2284
Keywords:
Augmentasi, Convolutional Neural Network (CNN), Penyakit daun padi , Rapid Application Development (RAD) , Teknik Optimasi
Abstract
Budidaya padi memegang peranan penting dalam ketahanan pangan nasional, namun sering terhambat oleh serangan penyakit daun yang berdampak signifikan terhadap penurunan produksi. Untuk menjawab tantangan tersebut, penelitian ini merancang sebuah aplikasi berbasis algoritma Convolutional Neural Network (CNN) guna mengklasifikasi penyakit daun padi secara otomatis dan akurat. Pengumpulan data dilakukan melalui observasi langsung di Gapoktan (Gabungan Kelompok Tani) Kabupaten Kuningan, wawancara dengan petani, studi literatur, serta pengembangan sistem menggunakan pendekatan Rapid Application Development (RAD) yang memungkinkan pembangunan aplikasi secara cepat dan terstruktur. Model Convolutional Neural Network (CNN) yang dibangun diuji menggunakan 480 gambar sampel dan menghasilkan akurasi tinggi sebesar 97,75%. Nilai F1-Score yang diperoleh yaitu 0,97 untuk Brown Spot, 0,921 untuk Blast, 0,871 untuk Hispa, dan 0,952 untuk daun sehat. Hasil ini menunjukkan bahwa aplikasi mampu mendeteksi penyakit secara dini, sehingga petani dapat segera mengambil tindakan preventif untuk meminimalkan kerugian hasil panen. Untuk meningkatkan performa, disarankan penerapan Model teknik optimasi yang diterapkan dalam proses model CNN (Convolutional Neural Network ) seperti perluasan dataset, variasi teknik augmentasi data set, serta evaluasi terhadap gambar dengan kompleksitas tinggi. Pengembangan ke klasifikasi penyakit lainnya juga sangat potensial. Secara keseluruhan, aplikasi ini berpeluang besar mendukung pertanian digital dan mewujudkan sistem pertanian padi yang lebih berkelanjutan dan modern.
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