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 (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022)
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.