Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
EditorsS. Slamin, Antonius Cahya Prihandoko, Fahrobby Adnan, Beny Prasetyo, Paramitha Nerisafitra, Hendra Yufit Riskiawan, Endang Sulistiyani, Prawidya Destarianto
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-73
Number of pages5
ISBN (Electronic)9781728134369
DOIs
Publication statusPublished - Oct 2019
Event2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019 - Jember, Indonesia
Duration: 16 Oct 201917 Oct 2019

Publication series

NameProceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019

Conference

Conference2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
Country/TerritoryIndonesia
CityJember
Period16/10/1917/10/19

Keywords

  • Indonesian banknotes
  • accuracy
  • artificial intelligence
  • classification
  • image processing
  • machine learning

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