Implementation of YOLOv8 as an Android-Based Rupiah Banknote Nominal Detector
DOI:
https://doi.org/10.32664/smatika.v15i02.1545Keywords:
Android, Rupiah, YOLOv8Abstract
The number of visually impaired individuals in Indonesia reaches 1.5%, or around 4 million people, who often face difficulties in recognizing the denominations of rupiah banknotes. Although Bank Indonesia has added distinguishing features to the banknotes, this method is less effective due to limitations in understanding or the physical condition of the currency. Object detection technology, such as YOLOv8, offers a solution thanks to its advantages in accuracy and speed. This research employs the CRISP-DM approach, which includes six stages: business understanding to understand the needs of visually impaired individuals, data understanding to study the characteristics of the 2022 rupiah banknote dataset, data preparation to prepare 5,435 images of 8 currency denominations (1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 75,000, and 100,000), modeling by training the YOLOv8n model, evaluation to assess model performance using a confusion matrix, and deployment on an Android application capable of real-time currency denomination detection through the camera. The evaluation results show an accuracy of 0.98, a precision of 0.988, a recall of 0.993, an average mAP50 score of 0.994, and an mAP50-95 score of 0.955, indicating that this model is quite effective in helping visually impaired individuals recognize the denominations of rupiah banknotes.
References
[1] A. M. Ramli and S. Gatra, “Fasilitas Akses Penyandang Tuna Netra atas Objek Hak Cipta Berdasar Marrakesh Treaty,” Kompas. Accessed: May 22, 2023. [Online]. Available: https://nasional.kompas.com/read/2023/01/15/16070311/fasilitas-akses-penyandang-tunanetra-atas-objek-hak-cipta-berdasar?page=all
[2] A. Hermawan, L. Lianata, Junaedi, and A. R. K. Maranto, “Implementasi Machine Learning Sebagai Pengenal Nominal Uang Rupiah dengan Metode YOLOv3,” SATIN - Sains dan Teknologi Informasi, vol. 8, no. 1, pp. 12–22, Jun. 2022, doi: 10.33372/stn.v8i1.816.
[3] A. P. Anto, Z. Abidin, and A. B. Utomo, “Identifikasi Nominal Uang Kertas Untuk Tuna Netra Berbasis Mikrokontroller Dengan Sistem Suara,” JEECOM: Journal of Electrical Engineering and Computer, vol. 2, no. 2, pp. 1–6, Oct. 2020, doi: 10.33650/jeecom.v2i2.1303.
[4] A. Fachri, “Teori Uang Dalam Perspektif Abu Hamid Al-Ghazali dan Jhon Maynard Keynes serta Relevansinya terhadap Sistem Keuangan di Indonesia,” UIN Raden Intan Lampung, 2017.
[5] L. Kurniawati, S. K. Risandriya, and H. Wijanarko, “Pendeteksi Nominal Uang Kertas bagi Penyandang Tunanetra Menggunakan Neural Network,” Journal of Applied Electrical Engineering, vol. 3, no. 2, pp. 39–43, Dec. 2019, doi: 10.30871/jaee.v3i2.1821.
[6] O. Simeone, “A Brief Introduction to Machine Learning for Engineers,” Foundations and Trends® in Signal Processing, vol. 12, no. 3–4, pp. 200–431, 2018, doi: 10.1561/2000000102.
[7] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2023, pp. 7464–7475. doi: 10.1109/CVPR52729.2023.00721.
[8] Ultralytics, “Ultralytics - YOLOv8,” Github, Accessed: Oct. 30, 2024. [Online]. Available: https://github.com/ultralytics/ultralytics
[9] B. Selcuk and T. Serif, “A Comparison of YOLOv5 and YOLOv8 in the Context of Mobile UI Detection,” 2023, pp. 161–174. doi: 10.1007/978-3-031-39764-6_11.
[10] Z. S. Jannah and F. A. Sutanto, “Implementasi Algoritma YOLO (You Only Look Once) Untuk Deteksi Rias Adat Nusantara,” Jurnal Ilmiah Universitas Batanghari Jambi, vol. 22, no. 3, p. 1490, Oct. 2022, doi: 10.33087/jiubj.v22i3.2421.
[11] P. A. Lestari Zakaria, M. Zainal, and M. Masnur, “Pengenalan Nilai Mata Uang Kertas Untuk Tunanetra Berbasis Android,” Jurnal Sintaks Logika, vol. 3, no. 3, pp. 40–44, Sep. 2023, doi: 10.31850/jsilog.v3i3.2587.
[12] K. M. Azhar, I. Santoso, and Y. A. A. Soetrisno, “IMPLEMENTASI DEEP LEARNING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DAN ALGORITMA YOLO DALAM SISTEM PENDETEKSI UANG KERTAS RUPIAH BAGI PENYANDANG LOW VISION,” Transient: Jurnal Ilmiah Teknik Elektro, vol. 10, no. 3, pp. 502–509, Sep. 2021, doi: 10.14710/transient.v10i3.502-509.
[13] F. Agustina, “Deteksi Kematangan Buah Pepaya Menggunakan Algoritma YOLO Berbasis Android,” Jurnal Ilmiah Infokam, vol. 18, no. 2, pp. 70–78, Dec. 2022, doi: 10.53845/infokam.v18i2.320.
[14] M. R. Sholahuddin et al., “Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy,” Jurnal Online Informatika, vol. 8, no. 2, pp. 261–270, Dec. 2023, doi: 10.15575/join.v8i2.1196.
[15] Teamkito Labs, “Uang Computer Vision Project,” Teamkito Labs. Accessed: Oct. 12, 2024. [Online]. Available: https://universe.roboflow.com/teamkito-labs/uang-wfprv
[16] H. Huang, B. Wang, J. Xiao, and T. Zhu, “Improved small-object detection using YOLOv8: A comparative study,” Applied and Computational Engineering, vol. 41, no. 1, pp. 80–88, Feb. 2024, doi: 10.54254/2755-2721/41/20230714.
[17] M. Hussain, YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision. 2024. doi: 10.48550/arXiv.2407.02988.
[18] C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Comput Sci, vol. 181, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.
[19] K. KOCAKANAT and T. SERİF, “Turkish Traffic Sign Recognition: Comparison of Training Step Numbers and Lighting Conditions,” European Journal of Science and Technology, Nov. 2021, doi: 10.31590/ejosat.1015972.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 SMATIKA JURNAL
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
