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All Journal Jurnal Informatika Universitas Pamulang Conference on Innovation and Application of Science and Technology (CIASTECH) KLIK: Kajian Ilmiah Informatika dan Komputer Scientific Student Journal for Information, Technology and Science
Deden Wahiddin
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Deteksi Nominal Mata Uang Rupiah Menggunakan Metode Convolutional Neural Network dan Feedforward Neural Network Dede Aprillia; Tatang Rohana; Tohirin Al Mudzakir; Deden Wahiddin
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1711

Abstract

This research aims to develop a nominal detection system for the Rupiah currency for the 2022 emission year using the Convolutional Neural Network (CNN) and Feedforward Neural Network (FNN) methods, especially in the context of applications for vending machines. This research explores the potential of computer vision technology to facilitate the introduction of Rupiah banknotes and contribute to the development of vending machines. The dataset used includes variations in lighting conditions, orientation, and position of banknotes, thus involving various augmentation and preprocessing processes. The model evaluation results include nominal detection accuracy in various conditions, considering the success of the system to support the performance of the vending machine. This research is expected to contribute to the development of more comprehensive technology and expand the application of CNN and FNN in the context of currency detection. In this research, the CNN method produced the best accuracy of 100% for testing in bright conditions, then in sufficient light conditions it produced an accuracy of 96.43%. Meanwhile, testing in dark conditions got quite low results, only 78.56%. Then the FNN method produces the same accuracy of 53.57% in bright light, sufficient light and low light conditions.
Co-Authors Abdul Majid Adi Rizky Pratama Ahmad Fauzi Ai Yanti Aldi Balkar amril siregar Andri Safari ARI SETIAWAN Ariel Ismail Baihaqi, Kiki Ahmad Candra Zonyfar Cici Emilia Sukmawati Dede Aprillia Dedy Prasetyo Pambudi Deny Adi Faldano Didik Remaldhi Dimas Maulana DWI KUSUMANINGRUM Dwi Sulistya Kusumaningrum Erik Candra Fauzi Euis Nurlaelasari Farkhina Dwi Utari Hanny Hikmayanti Handayani Hilman Fahrul Rahman Indra, Jamaludin Irpan Sulaeman Iyan Khoerniyah Kiki Wahyuddin Lestari, Santi Arum Puspita Lutfi Aziz Muhammad Ashi Dicky Muhammad Imam Naufal Murniasih ra Rahmat Raden Gumilar Riyansyah Rahmat Rahmat Santi Lestari Siregar, Amril Mutoi Tatang Rohana Tohirin Al Mudzakir Yana Cahyana Yulia Ningsih