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All Journal JOIV : International Journal on Informatics Visualization Journal of Research and Investigation in Education
Adif, Anjjani Mardhika
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Computer Science & IT Education Mathematics Other

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Journal : JOIV : International Journal on Informatics Visualization

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Automatic Feature Extraction of Marble Fleck in Digital Beef Images to Support Decision Preferences Pranata, Feriantano Sundang; Adif, Anjjani Mardhika; Na'am, Jufriadif
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2813

Abstract

Beef is one of the essential food ingredients to meet human nutritional needs. These nutrients are fundamental to the growth and development of the human body. The primary nutrient found in beef is protein. The nutritional value of protein in beef can be observed by the quality of the beef itself. An indicator of the protein level is the amount of marbling or white streaks in the meat. Marbling is characterized by a marble-like pattern in the meat layers. This study aims to process beef images to automatically identify marbling. The data processed is secondary data obtained from Kaggle.com, consisting of 60 images with a resolution of 800 by 800 pixels. This study develops a highly subjective method to produce fast and accurate classification. The processing stages used are pre-processing, segmentation, and extraction. The automatic stage is in the extraction, by developing a filtering algorithm. The results of this study can identify the marbling fleck ratio of each beef image very well, where each beef image has marbling flecks. The area of marbling flecks varies greatly depending on the quality of the meat, with the lowest quality having a ratio of 1.0% and the highest being 71.39%. This ratio level becomes an indicator in determining the quality of the meat, which is the primary preference in making accurate decisions in selecting meat quality. Thus, this study can serve as an indicator in determining the appropriate meat preference choice.
Co-Authors Feriantano Sundang Pranata Na'am, Jufriadif Sefriani, Rini Wijaya, Indra