Klasifikasi Hama Pada Daun Sawi Menggunakan Convolutional Neural Network (CNN) Dengan Algoritma Xcaption dan Optimasi Adam
AbstractSawi (Brassica rapa) adalah sayuran yang populer di Indonesia, namun serangan hama sering kali menghambat produktivitasnya, mengurangi kualitas dan kuantitas panen. Identifikasi hama secara akurat sangat penting untuk pengendalian yang efektif, namun metode konvensional yang melibatkan pengamatan visual sering kali kurang efisien dan rentan terhadap kesalahan manusia. Oleh karena itu, penelitian ini memanfaatkan teknologi terbaru dalam pemrosesan citra dan machine learning untuk meningkatkan akurasi dan efisiensi dalam identifikasi hama pada daun sawi. Convolutional Neural Network (CNN) dengan arsitektur Xception yang dioptimalkan menggunakan algoritma Adam dipilih sebagai metode utama untuk klasifikasi hama daun sawi. Penelitian ini menggunakan dataset gambar daun sawi dari situs publik kaggle, dengan preprocessing yang dilakukan untuk memastikan kualitas dan konsistensi data. Setelah melalui proses augmentasi data dan pelatihan model, model CNN dilatih dengan ukuran batch 64, epoch 100, dan dropout 0,5. Evaluasi menggunakan confusion matrix menunjukkan akurasi pelatihan dan validasi mencapai 99,00%. Hasil menunjukkan bahwa model CNN yang dikembangkan berhasil mencapai akurasi pelatihan dan validasi sebesar 99,00%. Evaluasi model menggunakan confusion matrix menunjukkan bahwa model mampu mengklasifikasikan hama daun sawi dengan akurasi yang sangat tinggi, memberikan solusi yang efisien untuk pengendalian hama di pertanian sawi. Hasil ini menunjukkan bahwa penggunaan teknologi CNN dengan optimasi yang tepat dapat secara signifikan meningkatkan kualitas dan produktivitas hasil panen.
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