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Yolanda, Desta
Universitas Andalas
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Sistem Pendeteksi Nilai Nominal dan Keaslian Uang Kertas Rupiah untuk Penyandang Tunanetra berbasis Mikrokontroler Fadtinah, Fadtinah; Yolanda, Desta
CHIPSET Vol. 7 No. 01 (2026): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.7.01.59-66.2026

Abstract

Rupiah banknotes are legal tender used within the territory of the Unitary State of the Republic of Indonesia for buying and selling transactions. However, the circulation of counterfeit money in the community still occurs frequently, causing financial losses, especially for people with visual impairments. Blind people's limited ability to see makes them vulnerable to being the main target in counterfeit money fraud cases and cases of switched money. Based on these problems, it is necessary to develop a system that can identify the nominal value and authenticity of rupiah banknotes. This research is intended to facilitate blind people in identifying money when doing buying and selling transaction activities in order to avoid cases that have occurred before. The implemented system has four functions, namely user detection using the HC-SR04 Ultrasonic sensor to provide information on instructions for using the system within a distance of 10-50cm. The second function is money in/money out detection using EC-0141 IR sensor to open and close the system lid automatically. The third function is nominal value detection using the TCS3200 sensor. The fourth function is to detect the authenticity of the money using the UV GY-31 ML8511 sensor. The output of the system is a voice instruction on the use of the system and the detection results of the inserted money. The results of the detection process for the nominal value of money can distinguish all nominal values of money with an average success rate of 78.57% for new money conditions and 71.43% for old money conditions. As for the detection process of money status with an average success rate of 57.14%. This shows that the system is quite accurate in detecting although there are still frequent errors in detection.
Co-Authors Afriyanti Dwi Kartika Ananda, Melly Wasilah Anandika, Arrya Arrya Anandika Budi Rahmadya Derisma Derisma Derisma Derisma Dodon Yendri Egita Lorenza K Eno Marozi Fadtinah, Fadtinah Fatma, Nela Ferdian, Rian Harist Islami Hersyah*, Mohammad Hafiz Hersyah, Mohammad Hafiz Kasoep, Werman Lathifah Arief, Lathifah Muhammad Fauzan Azhim Muhammad Wafa Alhanif Muhammad, Fauzan Nefy Puteri Novani Nefy Puteri Novani Nefy Puteri Novani Nela Fatma Pratama, Haritsah Yudha Purwoko, Agus Rahmayani, Mentari Tri Indah Rahmi Eka Putri Ratna Aisuwarya Rezy Pratiwi Rifki Suwandi Rifki Suwandi Rio, Muhammad - Rizka Hadelina Tati Erlina Tati Erlina Werman Kasoep Werman Kasoep Werman Kasoep Yoan Purbolingga Yuni, Riva Andra Zahran, Dzaki