Classification of Rupiah to Help Blind with The Convolutional Neural Network Method

Octavian Ery Pamungkas; Puspa Rahmawati; Dhany Maulana Supriadi; Natasya Nur Khalika; Thofan Maliyano; Dicky Revan Pangestu; Eka Setia Nugraha; Mas Aly Afandi; Nurcahyani Wulandari; Petrus Kerowe Goran; Agung Wicaksono1

doi:10.29207/resti.v6i2.3852

Octavian Ery Pamungkas Institut Teknologi Telkom Purwokerto
Puspa Rahmawati Institut Teknologi Telkom Purwokerto
Dhany Maulana Supriadi Institut Teknologi Telkom Purwokerto
Natasya Nur Khalika Institut Teknologi Telkom Purwokerto
Thofan Maliyano Institut Teknologi Telkom Purwokerto
Dicky Revan Pangestu Institut Teknologi Telkom Purwokerto
Eka Setia Nugraha Institut Teknologi Telkom Purwokerto
Mas Aly Afandi Institut Teknologi Telkom Purwokerto
Nurcahyani Wulandari Institut Teknologi Telkom Purwokerto
Petrus Kerowe Goran Institut Teknologi Telkom Purwokerto
Agung Wicaksono1 Institut Teknologi Telkom Purwokerto

DOI: https://doi.org/10.29207/resti.v6i2.3852

Keywords: Blind, Currency, Deep Learning, CNN, Epoch

Abstract

Currency is an item humans require as a medium of exchange in transactions, including those with vision impairments. It can be challenging for certain blind people to identify currencies. This research aimed to help blind people identify nominal currency when in the transaction. Deep Learning with the CNN algorithm and preprocessing with a sequential model were used in this research. This algorithm is modeled as neurons in the human brain that communicate and learn patterns. Data collecting, preprocessing, testing, and evaluation are this research stage. Six hundred eighty-one datasets are used, consisting of IDR 50.000, IDR 75.000, and IDR 100.000. Model testing was carried out with different iterations of 5, 10, 15, and 20 epochs. Different epoch values will affect the time it takes the model to learn, but the length of the learning process will result in more accurate models. The highest result obtained from all epoch tests is 100%. The class prediction results for the 69 test data show that they can be predicted based on the actual class, indicating that the model is adequate. The results of this classification might be used to construct a smartphone app that would assist visually challenged people in recognizing the nominals.

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