Klasifikasi Citra Hasil Potret Daun Tanaman Jagung Menggunakan Transfer Learning Deep Learning

Zendi Iklima; Yananto Mihadi Putra

doi:10.22441/jitkom.v8i2.008

Klasifikasi Citra Hasil Potret Daun Tanaman Jagung Menggunakan Transfer Learning Deep Learning

Zendi Iklima, Yananto Mihadi Putra

Abstract

Penyakit pada tanaman jagung dapat mempengaruhi produksi jagung,menghambat pertumbuhan tanaman jagung, penurunan kualitas jagung, dan kelangkaan jagung yang memicu kenaikan harga jagung. Hama serangga menjadi salah satu ancaman pada tanaman jagung. Kerusakan yang diakibatkan oleh hama serangga dapat dirasakan secara langsung dengan adanya daun – daun yang berlubang. Pendeteksian lebih dini dapat membantu para petani untuk memaksimalkan hasil panen. Teknik pembelajaran mesin menjadi salah satu teknologi yang telah digunakan untuk menyederhanakan proses klasifikasi penyakit pada daun. Dataset yang digunakan ialah hasil potret citra pada daun tanaman jagung yang sehat dan berlubang akibat hama. Dari beberapa metode yang ada, Convolutional Neural Network (CNN) menjadi metode yang paling optimal dalam domain klasifikasi tersebut. Peneltian ini menggunakan model deep learning terlatih yaitu transfer learning model yang sudah dilatih menggunakn berbagai macam dataset yang beragam. Adapun beberapa transfer learning model seperti MobileNet, VGG, Inception, dan lainnya. Hasil pengujian menunjukkan bahwa model VGG16 dan model Inception_V3 memiliki nilai akurasi paling tinggi dengan nilai akurasi 94.44%, dilanjutkan dengan model MobileNet dengan nilai akurasi 91.67%, model Xception dengan nilai akurasi 88.89%, dan model Inception_ResNet_V2 memiliki nilai akurasi paling rendah dengan nilai akurasi 87.50%.

Keywords

Citra; Convolutional Neural Network (CNN); Daun; Jagung; Transfer Learning Model

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DOI: http://dx.doi.org/10.22441/jitkom.v8i2.008

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