Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas

Julia Ulfah, Munirul Ula, Fajriana Fajriana, Nurdin Nurdin

Sari


The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.

Teks Lengkap:

PDF

Referensi


P. D. Arnesia, N. A. Pratama, and F. Sjafrina, “Aplikasi Artificial Intelligence Untuk Mendeteksi Objek Berbasis Web Menggunakan Library Tensorflow Js, React Js Dan Coco Dataset,†JSiI (Jurnal Sist. Informasi), vol. 9, no. 1, pp. 62–69, 2022, doi: 10.30656/jsii.v9i1.4243.

A. Prima, D. B. Santoso, and L. Nurpulaela, “Deteksi Otomatis Nominal Uang Kertas Rupiah Untuk Tunanetra Menggunakan Algoritma Arsitektur SSD Mobiilenetv3,†Teknokom, vol. 6, no. 2, pp. 151–159, 2022, doi: 10.31943/teknokom.v6i2.166.

L. A. Catyaningga, “Klasifikasi Uang Kertas Rupiah Berdasarkan Angka Nominal Secara Realtime Menggunakan Metode Single Shot Detector,†vol. 87, no. 1,2, pp. 149–200, 2023, [Online]. Available: https://repositorio.ufsc.br/xmlui/bitstream/handle/123456789/167638/341506.pdf?sequence=1&isAllowed=y%0Ahttps://repositorio.ufsm.br/bitstream/handle/1/8314/LOEBLEIN%2C LUCINEIA CARLA.pdf?sequence=1&isAllowed=y%0Ahttps://antigo.mdr.gov.br/saneamento/proees

A. Darmawan, I. G. N. G. A. Widyadhana, and E. H. Binugroho, “Implementasi Metode Deep Learning Pada Prototipe Validator Uang Rupiah,†Sebatik, vol. 26, no. 2, pp. 535–542, 2022, doi: 10.46984/sebatik.v26i2.2101.

K. Maulana Azhar, I. Santoso, D. Yosua, and A. A. Soetrisno, “Implementasi Deep Learning Menggunakan Metode Convolutional Neural Network Dan Algoritma Yolo Dalam Sistem Pendeteksi Uang Kertas Rupiah Bagi Penyandang Low Vision,†2021. [Online]. Available: https://ejournal3.undip.ac.id/index.php/transient

C. R. Gunawan, N. Nurdin, and F. Fajriana, “Deteksi Ikan Segar Secara Realtime dengan YOLOv4 menggunakan Metode Convolutional Neural Network,†J. Komtika (Komputasi dan Inform., vol. 7, no. 1, pp. 1–11, May 2023, doi: 10.31603/komtika.v7i1.8986.

J. Ulfah and N. Nurdin, “IMPLEMENTASI METODE DETEKSI TEPI CANNY UNTUK MENGHITUNG JUMLAH UANG KOIN DALAM GAMBAR MENGGUNAKAN OPENCV,†J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3, Aug. 2023, doi: 10.23960/jitet.v11i3.3147.

A. Rilo Pambudi, Garno, and Purwantoro, “JIP (Jurnal Informatika Polinema) DETEKSI KEASLIAN UANG KERTAS BERDASARKAN WATERMARK DENGAN PENGOLAHAN CITRA DIGITAL,†J. Inform. Polinema, vol. 6, no. 4, pp. 69–74, 2020.

C. R. Gunawan, Nurdin, and Fajriana, “Design of a Real-Time Object Detection Prototype System With YOLOv3 (You Only Look Once),†Int. J. Eng. Sci. Inf. Technol., vol. 2, no. 3, pp. 96–99, 2022, doi: 10.52088/ijesty.v1i4.309.

A. S. Riyadi, I. P. Wardhani, M. S. Wulandari, and S. Widayati, “Perbandingan Metode ResNet, YoloV3, dan TinyYoloV3 pada Deteksi Citra dengan Pemrograman Python,†PETIR, vol. 15, no. 1, pp. 135–144, Jan. 2022, doi: 10.33322/petir.v15i1.1302.

M. Sarosa and N. Muna, “IMPLEMENTASI ALGORITMA YOU ONLY LOOK ONCE (YOLO) UNTUK DETEKSI KORBAN BENCANA ALAM,†vol. 8, no. 4, 2021, doi: 10.25126/jtiik.202184407.

N. J. Hayati, D. Singasatia, and M. R. Muttaqin, “Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan,†Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 2, pp. 91–99, 2023, doi: 10.34010/komputa.v12i2.10654.

B. M. Saputra, M. Z. Ilman, M. Audina, M. Jepri, and P. Rosyani, “Sistem Pengenalan Tanda Lalu Lintas Menggunakan Algoritma YOLO,†J. Inov. dan Hum., vol. 1, no. 1, pp. 161–164, 2023, [Online]. Available: https://jurnalmahasiswa.com/index.php/Jurihum/article/view/264?

R. S. Immanuel Sihombing, W. Abadi Harahap, and W. Kurnia Rahman, “IMPLEMENTASI YOLO V8 UNTUK MENDETEKSI MATA UANG RUPIAH EMISI TAHUN 2022 BER-OUTPUT AUDIO,†JATI, 2024.

R. D. Djohari, H. R. Ngemba, S. Hendra, D. S. Angraeni, N. T. Lapatta, and D. W. Nugraha, “Employee Attendance System with Facial Recognition Technology Using a Single Shot Detector (SSD) Algorithm,†JITE, 2024, doi: 10.31289/jite.

V. A. Sutama, S. A. Wibowo, and R. Rahmania, “Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality,†J. Ilm. FIFO, vol. 12, no. 1, p. 1, Jul. 2020, doi: 10.22441/fifo.2020.v12i1.001.

H. Achmad, A. Pramudwiatmoko, M. Satrio Gumilang, B. Al Karim, and H. Wiyono, “Analisis Kinerja Model Deteksi Objek Yolo, Ssd, dan Faster R-Cnn pada Citra Penglihatan Malam untuk Pengenalan Tindak Kejahatan,†J. Teknol. Inf. dan Ilmu Komput., vol. 12, no. 1, pp. 145–152, 2025, doi: 10.25126/jtiik.2025128409.

A. M. E. Antara, S. A. Sari, N. Riswanti, D. A. Amin, V. Verdila, and A. P. A. Masa, “Deteksi Nominal Rupiah Uang Kertas Berdasarkan Citra Warna Menggunakan Segmentasi K-Means Clustering dan Klasifikasi Random Forest,†Kreat. Teknol. dan Sist. Inf., vol. 1, no. 1, pp. 34–39, 2023, doi: 10.30872/kretisi.v1i1.776.

T. Abuzairi, N. Widanti, A. Kusumaningrum, and Y. Rustina, “Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO,†J. RESTI, vol. 5, no. 4, pp. 624–630, Aug. 2021, doi: 10.29207/resti.v5i4.3184.

A. Ilyasa Samudra, D. Dhiya Ulhaq, T. Satrio, T. Zakaria, and P. Rosyani, “Perancangan Sistem Sederhana Pendeteksi Nominal Uang Rupiah Menggunakan Algoritma YOLOv8,†J. Artif. Intel. dan Sist. Penunjang Keputusan, vol. 1, no. 4, pp. 278–282, 2024, [Online]. Available: https://jurnalmahasiswa.com/index.php/aidanspk/article/view/1101

M. Ula and R. T. Adek, A Survey on The Accuracy of Machine Learning Techniques for Intrusion ad Anomaly Detection on Public Data Sets. IEEE, 2020.




DOI: http://dx.doi.org/10.30811/jaise.v5i4.7471

Refbacks

  • Saat ini tidak ada refbacks.


Indexing :

Creative Commons License
Journal of Artificial Intelligence and Software Engineering (JAISE) licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.