DISKRIMINASI CITRA KERUPUK KULIT BABI DAN SAPI MENGGUNAKAN THIRD GENERATION OF UIN SUNAN KALIJAGA’S HIGH POWER UV-LED FLUORESCENCE SPECTRO-IMAGING SYSTEM TERKOMBINASI DEEP LEARNING DENGAN ALGORITMA CNN

Meta Riani Ananda, NIM.: 18106020003 (2022) DISKRIMINASI CITRA KERUPUK KULIT BABI DAN SAPI MENGGUNAKAN THIRD GENERATION OF UIN SUNAN KALIJAGA’S HIGH POWER UV-LED FLUORESCENCE SPECTRO-IMAGING SYSTEM TERKOMBINASI DEEP LEARNING DENGAN ALGORITMA CNN. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Rambak are products derived from pig and cow skins. The physical similarity of them has potential for counterfeiting cow skin with pig skin. Fourier Transform Infrared (FTIR) spectroscopy, PCR DNA, and Artificial Neural Network (ANN) methods have been used to differentiate pig and cow rambak, but FTIR spectroscopy and PCR DNA methods were relatively expensive while the result of ANN method was not good. This study aimed to acquire and discriminate fluorescence images of pig and cow rambak using a third generation of UIN Sunan Kalijaga's high power UV-LED fluorescence spectro-imaging system combined with Deep Learning using CNN algorithm. This research was conducted by collecting and processing data. Data was collected by acquiring fluorescence images of pig and cow rambak using third generation of UIN Sunan Kalijaga's high power UV-LED fluorescence spectro-imaging system until obtained 220 fluorescence images of pig and cow rambak. Data processing began with the preparation of tools and materials, preprocessing, making Deep Learning, training and validation, and testing. Training of Deep Learning was carried out using each of 80 fluorescence image training data of pig and cow rambak, while the validation used each of 20 fluorescence image validation data of pig and cow rambak. The test used 10 fluorescence image test data of pig rambak and 10 fluorescence image test data of cow rambak. The results showed that the fluorescence images of pig and cow rambak were successfully acquired using third generation of UIN Sunan Kalijaga's high power UV-LED fluorescence spectro-imaging system and well discriminated by Deep Learning using CNN algorithm with 100% accuracy.

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