DIFERENSISASI KULIT BABI DAN SAPI BERBASIS NILAI RGB MENGGUNAKAN HIGH POWER UV-LED FLUORESCENCE IMAGING SYSTEM TERKOMBINASI MACHINE LEARNING (ML) BERALGORITMA SUPPORT VECTOR MACHINE (SVM)

Amar Hanif, NIM.: 17106020031 (2024) DIFERENSISASI KULIT BABI DAN SAPI BERBASIS NILAI RGB MENGGUNAKAN HIGH POWER UV-LED FLUORESCENCE IMAGING SYSTEM TERKOMBINASI MACHINE LEARNING (ML) BERALGORITMA SUPPORT VECTOR MACHINE (SVM). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

This research was motivated by the using of pork skin in craft and food products, so it requires a system that can easily differentiated pork skin and cow skin. This research aims to create and test a differentiation system for RGB value of pig skin and cow skin fluorescence images using machine learning. Data processing was carried out by training 160 RGB data values of pork skin and cow skin fluorescence images using the SVM algorithm to produce a model. Model testing was carried out using 40 RGB data values of pig skin and cow skin fluorescence images. The results of the research show that the RGB values of pork skin and cow skin fluorescence images have been successfully differentiated using machine learning with the SVM algorithm with an accuracy of 75% from the linear kernel, 75% from the 1st degree polynomial kernel, 77,5% from the 2nd degree polynomial kernel, 67,5 % of the degree 3 kernel polynomial and 70% of the degree 4 kernel polynomial. The results of machine learning with the SVM algorithm do not have good enough accuracy values, so it is recommended to use machine learning with other algorithms.

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