Perbandingan VGG16, ResNet50, dan EfficientNet-B0 untuk Klasifikasi Kematangan Jeruk Bali

Authors

  • Raditya Bagja Saputra Universitas Muhammadiyah Sukabumi, Jawa Barat, Indonesia
  • Asril Adi Sunarto Universitas Muhammadiyah Sukabumi, Jawa Barat, Indonesia
  • Didik Indrayana Universitas Muhammadiyah Sukabumi, Jawa Barat, Indonesia

DOI:

https://doi.org/10.62411/tc.v25i3.16870

Abstract

Kematangan buah jeruk bali menentukan kualitas dan harga jualnya di pasaran, namun klasifikasi secara manual sering kali bersifat subjektif dan rawan kesalahan. Penelitian ini bertujuan untuk membandingkan performa tiga arsitektur Convolutional Neural Network (CNN) berbasis transfer learning, yaitu VGG16, ResNet50, dan EfficientNet-B0, dalam mengklasifikasikan kematangan jeruk bali ke dalam kategori matang dan belum matang. Sebanyak 700 gambar lokal digunakan dan dibagi dengan rasio 70:15:15 untuk data pelatihan, validasi, dan pengujian. Metode yang diusulkan meliputi praproses gambar, augmentasi data secara real-time, serta fine-tuning model menggunakan bobot ImageNet sebelum diimplementasikan ke dalam antarmuka web Streamlit. Hasil evaluasi pada 105 gambar uji menunjukkan bahwa VGG16 memperoleh akurasi tertinggi sebesar 96,19%. Model ResNet50 mencatatkan waktu inferensi tercepat, yaitu 103,98 ms/gambar dengan akurasi 94,29%, sementara EfficientNet-B0 memiliki ukuran penyimpanan paling efisien (30,3 MB) dengan akurasi 88,57%. Dapat disimpulkan bahwa arsitektur VGG16 merupakan solusi otomatis paling optimal untuk klasifikasi kematangan jeruk bali guna meminimalkan kerugian pascapanen.   Kata kunci - Convolutional Neural Network, Kematangan Buah, Jeruk Bali, Transfer Learning, VGG16

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Published

2026-08-26

Issue

Vol. 25 No. 3 (2026): August 2026

Section

Articles

License

Copyright (c) 2026 Raditya Bagja Saputra, Asril Adi Sunarto, Didik Indrayana

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