Klasifikasi Penyakit Daun Apel Menggunakan Arsitektur CNN dengan Transfer Learning

Aulia Tegar Rahman, Arief Setyanto, Hanif Al Fatta

Abstract

Salah satu hasil produk pertanian subtropis yang dapat ditanam di Indonesia adalah apel. Dalam budidaya apel, pengendalian hama dan penyakit merupakan salah satu faktor kunci dalam perkembangan tanaman apel, karena dapat mempengaruhi hasil apel. Salah satu teknologi yang berkembang pesat dalam pendeteksian atau diagnosis penyakit tanaman dapat menyederhanakan proses klasifikasi penyakit tanaman khususnya penyakit daun apel dan membantu dalam diagnose dini adalah deep learning. Terdapat salah satu arsitektur deep learning yang dapat digunakan dalam klasifikasi citra, salah satunya Convolutional Neural Networks (CNN). Arsitektur CNN dengan transfer learning yang menghasilkan nilai akurasi yang masih bisa diterima, waktu yang diperlukan pendek pada klasifikasi penyakit daun apel. Hasil dari klasifikasi penyakit daun apel dengan VGG16 mendapatkan akurasi sebesar 99,31 %.

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DOI: https://doi.org/10.31284/j.senopati.2024.v6i1.6574

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