Implementasi Convolutional Neural Network Untuk Klasifikasi Penyakit Daun Jeruk Siam Madu Berbasis Citra Digital

Authors

  • Jesika Zandra Prastika Wahyudi Universitas Nusantara PGRI Kediri Indonesia
  • Danang Wahyu Widodo Universitas Nusantara PGRI Kediri Indonesia
  • Patmi Kasih Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/c142n550

Abstract

Penyakit daun jeruk dapat menurunkan produktivitas tanaman sehingga diperlukan metode identifikasi yang cepat dan akurat. Penelitian ini bertujuan untuk mengklasifikasikan penyakit daun jeruk siam madu berdasarkan citra digital menggunakan metode Convolutional Neural Network (CNN). Dataset yang digunakan terdiri dari 4177 citra yang dikelompokkan ke dalam empat kelas, yaitu Blackspot, Canker, Greening, dan Sehat. Tahapan penelitian meliputi preprocessing, augmentasi data, pembagian dataset, pelatihan model CNN, serta pengujian dan evaluasi performa model. Arsitektur CNN yang digunakan terdiri dari lima lapisan Conv2D yang dikombinasikan dengan Batch Normalization, MaxPooling2D, Global Average Pooling, Dense Layer, dan Dropout. Hasil pengujian menunjukkan model memperoleh accuracy sebesar 83,94%, precision sebesar 84,12%, recall sebesar 83,94%, dan F1-score sebesar 83,86%. Hasil tersebut menunjukkan bahwa CNN mampu mengenali karakteristik visual penyakit daun jeruk siam madu dan dapat digunakan sebagai metode klasifikasi berbasis citra digital.

Keywords:

Convolutional Neural Network, Citra Digital, klasifikasi citra, penyakit daun jeruk, deep learning.

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Published

2026-07-20

Issue

Vol. 10 No. 2 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

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How to Cite

Implementasi Convolutional Neural Network Untuk Klasifikasi Penyakit Daun Jeruk Siam Madu Berbasis Citra Digital. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(2), 1063-1072. https://doi.org/10.29407/c142n550