TY - GEN
T1 - The Application of Artificial Neural Network (ANN) for Meat Classification Based on Near Infrared (NIR) Data Spectroscopy
AU - Damayanti, Ayu Anisa
AU - Suyanto,
AU - Nasution, Aulia M.T.
AU - Stendafity, Selfi
AU - Shoffiana, Nur Alfiani
AU - Hartati, Ayu Dian
AU - Susanti, Mia Dwi
AU - Kartika Putri, Nabilah
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Research has been conducted on the classification of different types of meat using Near Infrared (NIR) spectroscopy method. This research uses 6 types of meat, namely, beef, chicken, lamb, chicken liver, ear mushrooms (A. Auriculla), and oyster mushrooms (P. Ostretatus). The input data used to design the ANN (Artificial Neural Network) architecture is the wavelength and transmittance intensity of each sample type. ANN architecture was designed by creating 1 input layer, 8 hidden layers, and 1 output layer and getting the best results. The ANN classifies the different types of meat so that meat differentiation can be done. Classification gives an accuracy value 72.38% with an epoch of 500. This result is good to be able for distinguish and predict different types of meat. The result shows that ANN can contribute with high accuracy and precision for NIR spectroscopy data.
AB - Research has been conducted on the classification of different types of meat using Near Infrared (NIR) spectroscopy method. This research uses 6 types of meat, namely, beef, chicken, lamb, chicken liver, ear mushrooms (A. Auriculla), and oyster mushrooms (P. Ostretatus). The input data used to design the ANN (Artificial Neural Network) architecture is the wavelength and transmittance intensity of each sample type. ANN architecture was designed by creating 1 input layer, 8 hidden layers, and 1 output layer and getting the best results. The ANN classifies the different types of meat so that meat differentiation can be done. Classification gives an accuracy value 72.38% with an epoch of 500. This result is good to be able for distinguish and predict different types of meat. The result shows that ANN can contribute with high accuracy and precision for NIR spectroscopy data.
KW - ANN
KW - Accuracy
KW - Meat
KW - NIR
KW - Spectra
UR - http://www.scopus.com/inward/record.url?scp=85186501108&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427612
DO - 10.1109/ICAMIMIA60881.2023.10427612
M3 - Conference contribution
AN - SCOPUS:85186501108
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 864
EP - 868
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -