Identifikasi dan Klasifikasi Penyakit Daun Jagung Menggunakan Algoritma CNN sebagai Media Pembelajaran IPA
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Indonesia merupakan negara agraris dengan potensi besar di sektor pertanian, salah satunya pada komoditas jagung (Zea mays). Permasalahan umum yang dihadapi petani jagung adalah serangan penyakit pada daun, seperti bercak daun, hawar daun, dan karat daun, yang dapat menurunkan produktivitas tanaman. Penelitian ini bertujuan untuk mengembangkan aplikasi berbasis deep learning dengan algoritma Convolutional Neural Network (CNN) guna mengidentifikasi dan mengklasifikasikan penyakit daun jagung secara real-time. Aplikasi dirancang dalam bentuk hybrid agar dapat diakses melalui perangkat Android maupun iOS, serta berfungsi sebagai media pembelajaran interaktif pada mata pelajaran IPA, khususnya bidang agronomi. Hasil pengujian model CNN terhadap dataset uji menunjukkan akurasi sebesar 88%, sementara pengujian terhadap 100 data lapangan menggunakan metode confusion matrix menghasilkan akurasi sebesar 89%. Temuan ini menunjukkan bahwa penerapan teknologi CNN efektif dalam klasifikasi penyakit daun jagung serta berpotensi meningkatkan kualitas pembelajaran berbasis teknologi di bidang sains alam.
Identification and Classification of Corn Leaf Diseases Using CNN Algorithm as a Learning Media for Science Education
Abstract
Indonesia is an agrarian country with significant potential in the agricultural sector, including corn (Zea mays) as a major source of carbohydrates and protein besides rice. One of the common problems faced by corn farmers is leaf disease, such as leaf spot, leaf blight, and rust, which can significantly reduce crop productivity. This study aims to develop a deep learning-based application using the Convolutional Neural Network (CNN) algorithm to identify and classify corn leaf diseases in real-time. The application is built as a hybrid platform, making it accessible on both Android and iOS devices. In addition to functioning as a diagnostic tool for farmers, the application serves as an interactive learning medium for science education, particularly in agricultural topics. The CNN model achieved an accuracy of 88% on the test dataset, and further evaluation using 100 real-field data samples and a confusion matrix yielded an accuracy of 89%. These findings demonstrate the effectiveness of CNN in image-based disease classification and its potential to enhance the quality of technology-assisted learning in natural science education.
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