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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) AMMA : Jurnal Pengabdian Masyarakat
Solihudin, Dodi
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Humanities Chemistry Computer Science & IT Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Journal : Jurnal Teknik Informatika (JUTIF)

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Data Augmentation Techniques on the Accuracy of Fertile and Infertile Egg Classification Using Convolutional Neural Networks Nurhakim, Bani; Solihudin, Dodi; Amalia, Dina; Arelia, Irly
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5234

Abstract

The classification of fertile and infertile chicken eggs is crucial in the poultry industry to ensure optimal incubation efficiency and hatchability. However, the visual similarity between both egg types under candling conditions poses a significant challenge for manual inspection. This study aims to develop a convolutional neural network (CNN) model using the EfficientNetB4 architecture to automatically classify egg fertility based on image data. The dataset comprises candling images of chicken eggs, which underwent preprocessing steps such as resizing, normalization, and histogram stretching to enhance contrast. To improve model generalization, aggressive data augmentation techniques were applied, including rotation, flipping, zooming, and brightness adjustment. The model was trained in two phases—feature extraction and fine-tuning—using transfer learning and class balancing strategies. Evaluation results demonstrated high performance with an F1-score of 0.95 and balanced classification across both classes. The model's interpretability was further enhanced using Grad-CAM visualization, showing relevant activation regions. These findings indicate that the proposed method is effective in automating egg fertility classification and has potential for broader application in agricultural image diagnostics.
Co-Authors Adhari Febrian, Mochamad Adniana, Royana Ahmad Faqih Arelia, Irly Arief Saputra, Mochamad Arya Wijaya, Arya Damayanti Damayanti Dina Amalia Eka Permana, Sandy Fathurrohman, Fathurrohman Fazari Hidayat, Nizar Hagi Badra, Muhammad Haris Maulana Ibrahim, Mochammad Herdiana, Ruli Indriya Efendi, Dendy Irfan Ali, Irfan Liawati, Ayu Lukman Rohmat, Cep Muhamad Fajid, Marfelio Narasati, Riri Narasati Nur alamsyah, Deri Nurhakim, Bani Pebdika, Angga Pratama, Denni Prihantono, Willy Puri, Anita Putri Nindya, Nazwa Rifa’I, Ahmad Rohmat, Cep Lukman Saeful Anwar, Saeful Sajidah, Sajidah Tohidi, Edi Wahyudin, Edi Yulistiano, Irena