Implementasi Deep Learning Menggunakan Metode  Convolutional Neural Network Dan Algoritma Yolo Dalam  Sistem Pendeteksi Uang Kertas Rupiah Bagi Penyandang  Low Vision

Azhar, Kevin Maulana; Santoso, Imam; Soetrisno, Yosua Alvin Adi

doi:10.14710/transient.v10i3.502-509

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Berbagai penelitian mengenai sistem pendeteksi nominal uang kertas rupiah sebelumnya sudah pernah dilakukan. Salah satunya yaitu Implementasi Deep Learning menggunakan Metode Convolutional Neural Network dan Algoritma Yolo dalam sistem pendeteksi nominal uang kertas rupiah [2]. Pada penelitian ini berfokus pada Metode CNN sebagai feature extractor dan Yolo sebagai algoritma dalam mendeteksi objek.…”

Section: Pendahuluanunclassified

Deteksi Otomatis Nominal Uang Kertas Rupiah Untuk Tunanetra Menggunakan Algoritma Arsitektur SSD Mobiilenetv3

Prima,

Santoso,

Nurpulaela

2022

teknokom

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Money is a legal tender in the buying and selling transactions of society. However, visually impaired people face visual limitations that affect their ability to recognize the value of Rupiah banknotes. Although Rupiah banknotes already have a blind code feature, this is often ineffective due to the condition of the banknotes. In this study, we use Artificial Intelligence technology, especially deep learning, to help visually impaired people recognize the value of Rupiah banknotes more easily. Our system is built using Convolutional Neural Network (CNN) technique with MobileNetV3 architecture and Single Shot Multibox Detector (SSD) algorithm. The results show that our system is able to operate well under various lighting conditions, including daytime and nighttime. Under sufficient lighting conditions, the system achieves an accuracy of between 80% to 95%. However, we acknowledge that in low-light or nighttime conditions, our system has problems detecting banknotes. Thus, this research contributes to the effort of improving the accessibility of visually impaired people in recognizing Rupiah banknotes, although we also recognize the need for more attention to address low lighting conditions in future development.

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Section: Pendahuluanunclassified

Deteksi Otomatis Nominal Uang Kertas Rupiah Untuk Tunanetra Menggunakan Algoritma Arsitektur SSD Mobiilenetv3

Prima,

Santoso,

Nurpulaela

2022

teknokom

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“…However, in practice, currency in circulation often lacks uniformity and may show signs of wear and tear. The method still has several weaknesses, namely in terms of memory capacity, physical condition, and the absence of a determinant of honesty in the process of buying and selling goods and services [4]. Previous studies have employed the TCS3200 color sensor to determine the value of banknotes; however, the performance of the system heavily relies on the physical condition of the currency [5].…”

Section: Introductionmentioning

confidence: 99%

Integrate Yolov8 Algorithm For Rupiah Denomination Detection In All-In-One Smart Cane For Visually Impaired

Kumara,

Jati,

Yuniari

2024

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The eyes are crucial tools for human observation and perception, facilitating various tasks in daily life. Individuals, including those with visual impairments or blindness, engage in currency transactions, posing challenges in recognizing notes and preventing mishaps with counterfeit money. Despite government efforts, features like embossing on banknotes have limited effectiveness due to the circulated currency's disheveled condition. Addressing the visually impaired community's needs is imperative. An innovative solution, the "all-in-one smart white cane," integrated with machine learning supports daily activities, enhancing independence for visually impaired individuals. The YOLOv8 algorithm is employed for the precise detection of monetary denominations, subsequently recorded through a camera and seamlessly integrated into a smart cane, resulting in a consolidated device. This device, designed with standout features, excels in detecting Indonesian Rupiah banknote denominations. Detection performance testing, incorporating methods like object rotation, utilized a dataset divided into training (70%), validation (20%), and test (10%) segments. Modifications to contrast and variability rotation are essential in the context of real-time nomination recognition. These adjustments are implemented to ensure accurate and swift identification in dynamic, real-world scenarios. Testing results reveal a 99% average accuracy in recognizing currency note denominations, presenting an effective solution for the visually impaired community.

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Classification of Currency Denominations in Folded Condition using Convolutional Neural Network

Hamami,

Amri,

Mutmainnah

2024

2024 7th International Conference of Computer and Informatics Engineering (IC2IE)

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Implementasi Deep Learning Menggunakan Metode Convolutional Neural Network Dan Algoritma Yolo Dalam Sistem Pendeteksi Uang Kertas Rupiah Bagi Penyandang Low Vision

Cited by 4 publications

References 3 publications

Deteksi Otomatis Nominal Uang Kertas Rupiah Untuk Tunanetra Menggunakan Algoritma Arsitektur SSD Mobiilenetv3

Deteksi Otomatis Nominal Uang Kertas Rupiah Untuk Tunanetra Menggunakan Algoritma Arsitektur SSD Mobiilenetv3

Integrate Yolov8 Algorithm For Rupiah Denomination Detection In All-In-One Smart Cane For Visually Impaired

Classification of Currency Denominations in Folded Condition using Convolutional Neural Network

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