Penerapan IoT dan CNN dengan Attention Layer untuk Pendeteksian Penyakit pada Daun Jagung Berbasis Gambar Digital
Abstrak
Penyakit pada daun jagung seperti Northern Leaf Blight, Common Rust, dan Gray Leaf Spot sering menyebabkan penurunan produktivitas panen secara signifikan. Identifikasi penyakit secara manual oleh petani di lapangan memiliki keterbatasan dalam aspek waktu, akurasi, dan subjektivitas pengamatan, sehingga rentan terjadi kesalahan diagnosis. Oleh karena itu, diperlukan sistem deteksi dini yang akurat dan efisien untuk meminimalisir risiko gagal panen. Penelitian ini mengembangkan sistem deteksi otomatis berbasis Deep Learning dengan arsitektur Convolutional Neural Network (CNN) MobileNetV3Large. Sistem dirancang menggunakan arsitektur Local Client-Server, di mana kamera smartphone difungsikan sebagai perangkat akuisisi citra yang praktis untuk mengirimkan data visual secara nirkabel ke server lokal, sedangkan proses klasifikasi berat dilakukan pada sisi server. Untuk mengoptimalkan performa model pada dataset yang tidak seimbang, penelitian ini menerapkan strategi Transfer Learning dengan penambahan teknik Class Weighting. Hasil eksperimen menunjukkan bahwa pelatihan model pada fase Transfer Learning (Base Model) mampu mencapai kinerja optimal tanpa memerlukan tahapan fine-tuning yang agresif, dengan perolehan akurasi validasi tertinggi sebesar 94,95%. Hasil ini membuktikan bahwa sistem yang dibangun mampu mendeteksi jenis penyakit daun jagung dengan presisi tinggi serta memiliki arsitektur komunikasi yang siap untuk diterapkan sebagai alat bantu diagnosis di lingkungan perkebunan.
Kata kunci— mobilenetv3, daun jagung, convolutional neural network, transfer learning, class weighting, client-server
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