Yulianti, Titin and Yudamson, Afri and Septama, Hery Dian and Sulistiyanti, Sri Ratna and Setyawan, FX. Arinto and Telaumbanua, Marely (2016) Meat Quality Classification Based on Color Intensity Measurement Method. In: International Symposium on Electronics and Smart Devices, 29-30 November 2016, Bandung.

Meat Quality Classification Based on Color Intensity Measurement Method(1).pdf

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The fresh and defective beef identification by consumers is subjectively through visual observation. However, identifying beef quality manually has disadvantage, there is human visual limitations, differences in human perception in assessing the quality of an object, and ability of each individual knowledge are different. Therefore, we need a technological device that can be applied to identify the quality of beef that can be used by people. The aim of this research is measuring the percentage of color intensity average from R, G, and B channel. The fresh and defective beef is identified using feature of the beef image. That feature is percentages of intensity average value from R (red), G (green), and B (blue) channel. The optimal feature is gotten based on the percentage values. The feature is gotten by using image processing method. The percentage of R channel intensity average value is defined, which can be used to classify the fresh and defective beef. The percentage of R channel intensity is consecutively decrease on every 4 hours. It is shown on each beef sample. The R channel of the fresh image has higher percentage of intensity average value than the defective beef. The fresh beef has 56.38% to 66.33% of the R channel intensity average. whereas the defective beef has 37.76% to 51.71% of the R channel intensity. Keywords—percentage of intensity average, beef quality classification, image pocessing.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Teknik (FT) > Prodi Teknik Elektro
Depositing User: Dr. Sri Ratna Sulistiyanti
Date Deposited: 28 May 2018 03:57
Last Modified: 28 May 2018 03:57
URI: http://repository.lppm.unila.ac.id/id/eprint/2849

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