TY - GEN
T1 - Accuracy Comparison of UV-filtered Indonesian Banknotes Denomination Recognition Systems
AU - Suwignyo, Andrianto
AU - Tjahyanto, Aris
AU - Samopa, Febriliyan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - As technology progresses, monetary transaction systems around the world are being continuously developed. Artificial intelligence as part of machine learning, especially, emerges as a new trend being used in transactions automation. This research is written with a purpose to propose a comprehensive comparison of accuracy, in recognizing denomination of authentic Indonesian Banknotes (Rupiah) using image processing methods and machine learning algorithms. This research is comparing accuracy between some classification systems designed using several known classifiers, using three kinds of image resolutions. From this research, KNN produced 100% accuracy, while the accuracy for SVM varied between 12.5 to 100% depending on the kernel used.
AB - As technology progresses, monetary transaction systems around the world are being continuously developed. Artificial intelligence as part of machine learning, especially, emerges as a new trend being used in transactions automation. This research is written with a purpose to propose a comprehensive comparison of accuracy, in recognizing denomination of authentic Indonesian Banknotes (Rupiah) using image processing methods and machine learning algorithms. This research is comparing accuracy between some classification systems designed using several known classifiers, using three kinds of image resolutions. From this research, KNN produced 100% accuracy, while the accuracy for SVM varied between 12.5 to 100% depending on the kernel used.
KW - Indonesian banknotes
KW - accuracy
KW - artificial intelligence
KW - classification
KW - image processing
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85077820907&partnerID=8YFLogxK
U2 - 10.1109/ICOMITEE.2019.8921098
DO - 10.1109/ICOMITEE.2019.8921098
M3 - Conference contribution
AN - SCOPUS:85077820907
T3 - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
SP - 69
EP - 73
BT - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
A2 - Slamin, S.
A2 - Prihandoko, Antonius Cahya
A2 - Adnan, Fahrobby
A2 - Prasetyo, Beny
A2 - Nerisafitra, Paramitha
A2 - Riskiawan, Hendra Yufit
A2 - Sulistiyani, Endang
A2 - Destarianto, Prawidya
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
Y2 - 16 October 2019 through 17 October 2019
ER -