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All Journal Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research
Syahrul Amin, Akhmad
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Humanities Economics, Econometrics & Finance Education Social Sciences Other

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Klasifikasi Uang Kertas Rupiah Baru Menggunakan Metode CNN Syahrul Amin, Akhmad; Riandi Zahra, Muhammad Alvin; Jibran, Kemal Fahreza; Puspita Sari, Anggraini
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 1 No. 4 (2024): AGUSTUS-OKTOBER 2024
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v1i4.2218

Abstract

This study introduces an advanced system for classifying Indonesian Rupiah (IDR) banknotes using Convolutional Neural Networks (CNNs). By harnessing sophisticated image processing techniques, the system analyzes surface patterns on banknotes and categorizes them based on distinct visual features to determine their denominations. The research utilizes a comprehensive dataset containing 1190 images across various IDR denominations, captured at a standardized resolution of 224x224 pixels. Prior to model training, meticulous data preprocessing techniques such as resizing, augmentation, and normalization are employed to optimize dataset quality and enhance model robustness. The CNN architecture is specifically designed with convolutional and pooling layers to automatically extract intricate features from banknote images, culminating in a softmax output layer for precise classification. Training the model through multiple epochs ranging from 50 to 150 showcases notable enhancements in accuracy, achieving up to 100% accuracy with minimal loss, indicative of its reliable performance. This research underscores the effectiveness of CNNs in automating banknote classification tasks, promising improved accuracy and efficiency in real-world applications.
Co-Authors Anggraini Puspita Sari Jibran, Kemal Fahreza Riandi Zahra, Muhammad Alvin