Egg Quality Classification Using Support Vector Machine Based on Image and Non-Image Fusion

Abrolillah, Faizal (Department of Computer Science, State Islamic University of Maulana Malik Ibrahim, Malang, 65144, Indonesia)
Santoso, Irwan Budi (Department of Computer Science, State Islamic University of Maulana Malik Ibrahim, Malang, 65144, Indonesia)
Chamidy, Totok (Department of Computer Science, State Islamic University of Maulana Malik Ibrahim, Malang, 65144, Indonesia)

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan

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.