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Lestari, Febbi Senna
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education

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Sistem Grading Kualitas Telur Ayam Konsumsi berdasarkan Citra Kerabang Menggunakan Convolutional Neural Putra, Fatra Nonggala; Lestariningsih, Lestariningsih; Tricahyo, Vion Age; Lestari, Febbi Senna
BRILIANT: Jurnal Riset dan Konseptual Vol 9 No 3 (2024): Volume 9 Nomor 3, Agustus 2024
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v9i3.2014

Abstract

Chicken egg grading is identifying and grouping or classifying chicken eggs for consumption based on specific criteria. In SNI 3926:2008, the grade/class of consumption eggs is divided into 3 (three): Grades I, II, and III. Eggs with grade I have a higher selling value than grade II. Likewise, grade II eggs have a higher selling value than grade III. With these criteria, it is necessary to carry out a grading process that is still carried out by manual observation, so it requires extra time and energy with less uniform results because it depends on the individual observing. Based on these problems, it is necessary to have an egg quality grading system based on shell images that can identify egg quality according to its grade. In the study, there are several stages of research, namely: 1) Literature Study, 2) Data Collection, 3) System Design, 4) System Testing, 5) Results Analysis, and 6) Publication and Reporting. The CNN algorithm is used to classify based on shell images. The study's results showed that the performance of the CNN model had an accuracy value of 92.19%, precision of 92.78%, recall of 92.62%, and f1-score of 92.42%. This shows that the CNN model can well classify the quality of chicken eggs for consumption based on shell images.
Co-Authors FATRA NONGGALA PUTRA Lestariningsih Lestariningsih Tricahyo, Vion Age