Penerapan Arsitektur Residual Network (ResNet-50) Pada Klasifikasi Citra Lovebird

Authors

  • Ryan Sea Prayoga Universitas Nusantara PGRI Kediri Indonesia
  • Danar Putra Pamungkas Universitas Nusantara PGRI Kediri Indonesia
  • Danang Wahyu Widodo Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/32z6z522

Abstract

Klasifikasi jenis lovebird berdasarkan citra digital merupakan tantangan karena kemiripan visual antarjenis, terutama pada warna bulu dan pola tubuh. Penelitian ini bertujuan membangun model klasifikasi otomatis menggunakan algoritma Convolutional Neural Network dengan arsitektur Residual Networks 50. Dataset terdiri dari 500 gambar lovebird yang dikategorikan ke dalam tiga jenis, dengan praproses normalisasi dan augmentasi citra. Model ResNet-50 dilatih selama 1000 epoch dan dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan model mencapai akurasi pelatihan 88,6% dan F1-score rata-rata 83,4% tanpa overfitting signifikan. Temuan ini membuktikan bahwa arsitektur ResNet-50 efektif dalam mengklasifikasikan jenis lovebird, serta menunjukkan potensi penerapan kecerdasan buatan dalam identifikasi spesies hewan peliharaan secara visual. 

Keywords:

lovebird, ResNet-50, CNN(Convolutional Neural Network), Citra digital

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References

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Published

2025-07-10

Issue

Vol. 9 No. 3 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

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Copyright (c) 2025 Ryan Sea Prayoga, Danar Putra Pamungkas, Danang Wahyu Widodo

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How to Cite

Penerapan Arsitektur Residual Network (ResNet-50) Pada Klasifikasi Citra Lovebird. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(3), 2038-2045. https://doi.org/10.29407/32z6z522