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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Chamidy, Totok
Department of Computer Science, State Islamic University of Maulana Malik Ibrahim, Malang, 65144, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Egg Quality Classification Using Support Vector Machine Based on Image and Non-Image Fusion Abrolillah, Faizal; Santoso, Irwan Budi; Chamidy, Totok
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26263

Abstract

Egg production and consumption in Indonesia continue to rise, highlighting the need for accurate egg quality assessment. This study evaluated egg quality using a Support Vector Machine (SVM) model that integrates image and non-image features through feature-level fusion. A total of 750 eggs were analyzed based on external characteristics (shell color, cleanliness, texture, weight, and images) and internal characteristics (odor, albumen, yolk, black spots, images). Image data were reprocessed through grayscale conversion, resizing, and texture extraction using the Gray Level Co-occurrence Matrix (GLCM). Both linear and polynomial SVM kernel with varying degrees were tested, and the polynomial kernel (degree 6) achieved the best, with 86% accuracy, 91% precision, and 87% recall. These results demonstrate that integrating image and non-image features significantly enhances egg quality classification compared to using either data type alone. These findings provide valuable insights for developing automated egg grading system in the poultry industry.
Co-Authors Abrolillah, Faizal Santoso, Irwan Budi