Multiclass Classification on Nominal Value of Rupiah Banknotes Based on Image Processing


Huzain Azis(1); Purnawansyah Purnawansyah(2); Nurul Alfiyyah(3*);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia, Universitas Kuala Lumpur
(3) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


This study aimed to classify the nominal value of Rupiah banknotes using image processing and classification methods. The research design was conducted by collecting a dataset of Rupiah banknotes consisting of 30 classes, each with 100 images. This research uses image preprocessing by using Canny Segmentation to create the edges of objects and clarify image details. The Hu Moments method, which describes the pixel distribution and shape of objects, was used to extract special features from images. Furthermore, classification modeling was carried out with Decision Tree and Random Forest to classify banknotes based on extracted characteristics. Model evaluation was carried out by measuring accuracy, precision, recall and f1-score performance and using cross-validation with k-fold = 5. The results showed that the Random Forest method was able to classify Rupiah banknotes well. In performance evaluation, the Random Forest method achieved an accuracy of 0.93 and good precision, recall, and f1-score scores for several banknote classes. The Decision Tree method also achieved good results, with an accuracy of 0.86. The results of the classification evaluation showed that the Random Forest method was better than the Decision Tree in classifying the banknotes.


  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 260 times
PDF view: 98 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v16i1.1784.87-99 		  

Cite

How to cite item

References


F. A. Mardha, S. Z. Salsabiila, S. K. Sayid, and W. Ariska, “Identifikasi Nilai Mata Uang Kertas Rupiah dengan Metode Ekstraksi Ciri Local Binary Pattern dan Metode Klasifikasi Naive Bayes,” Semin. Nas. Mhs. Ilmu Komput. dan Apl., vol. 3, no. 2, pp. 949–962, 2022.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.

A. R. Pratama, M. Mustajib, and A. Nugroho, “Deteksi Citra Uang Kertas dengan Fitur RGB Menggunakan K-Nearest Neighbor,” J. Eksplora Inform., vol. 9, no. 2, pp. 163–172, 2020, doi: 10.30864/eksplora.v9i2.336.

A. Priadana and A. W. Murdiyanto, “Metode SURF dan FLANNa untuk Identifikasi Nominal Uang Kertas Rupiah Tahun Emisi 2016 pada Variasi Rotasi,” J. Teknol. dan Sist. Komput., vol. 7, no. 1, pp. 19–24, 2019, doi: 10.14710/jtsiskom.7.1.2019.19-24.

N. Hadisukmana and N. P. Adri Yudianto, “Paper Money Recognizer Using Feature Descriptor,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 1, pp. 117–126, 2018, doi: 10.11591/ijeecs.v12.i1.pp117-126.

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

R. Alfita, A. F. Ibadilah, and A. Prianto, “Identifikasi Nilai Nominal Uang Kertas Berdasarkan Warna Berbasis Image Processing Menggunakan Metode Template Matching,” J. Tek. Elektro dan Komput. TRIAC, vol. 9, no. 1, pp. 28–32, 2022.

S. Rossa, Lindawati, and Suzanzefi, “Design & Build Banknote Nominal Identification Tools for Visual Impairment Using Convolutional Neural Network Algorithm and Tensorflow with Android Based,” J. Eng. Des. Technol., vol. 22, no. 3, pp. 244–252, 2022, doi: 10.31940.

A. I. Rauyani, M. H. Ibrahim, and S. Pramono, “ROI based Indonesian Paper Currency Recognition Using Canny Edge Detection,” J. Electr. Electron. Information, Commun. Technol., vol. 2, no. 1, pp. 5–8, 2020, doi: 10.20961/jeeict.2.1.41349.

H. Azis and S. Rahmah Jabir, “Implementasi Aset 3D Rumah Tongkonan Pada Desa Marinding,” Ilmu Komput. untuk Masy., vol. 4, no. 1, pp. 32–37, 2023, doi: 10.33096/ilkomas.v4i1.1552.

W. Mellyssa, “Pengenalan Nominal Uang Kertas menggunakan Jaringan Syaraf Tiruan Backpropagation,” J. Litek J. List. Telekomun. Elektron., vol. 16, no. 1, p. 1, 2019, doi: 10.30811/litek.v16i1.1463.

A.-K. Al-Khowarizmi, “Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network,” J. Informatics Telecommun. Eng., vol. 3, no. 2, pp. 258–265, 2020, doi: 10.31289/jite.v3i2.3284.

A. Pradinata, P. L. Lokapitasari Belluano, and H. Azis, “Perancangan Aplikasi E-Ticketing dengan Model Arsitektur Microservice Menggunakan Kafka,” Bul. Sist. Inf. dan Teknol. Islam, vol. 4, no. 3, pp. 286–295, 2023, doi: 10.33096/busiti.v4i3.1806.

Y. Salim, I. Muis, L. Syafie, H. Azis, and A. Rachman Manga, “One-gateway system in managing campus information system using microservices architecture,” Bull. Soc. Informatics Theory Appl., vol. 7, no. 2, pp. 83–91, 2023, doi: 10.31763/businta.v7i2.635.

Nurul A’ayunnisa, Y. Salim, and H. Azis, “Analisis Performa Metode Gaussian Naïve Bayes untuk Klasifikasi Citra Tulisan Tangan Karakter Arab,” Indones. J. Data Sci., vol. 3, no. 3, pp. 115–121, 2022, doi: 10.56705/ijodas.v3i3.54.

H. Azis and S. Rahmah Jabir, “Implementasi Aset 3D Rumah Tongkonan Pada Desa Marinding,” Ilmu Komput. untuk Masy., vol. 4, no. 1, pp. 32–37, 2023, doi: 10.33096/ilkomas.v4i1.1552.

A. M. Yusuf and S. Suyanto, “Authenticity and Nominal Detection of Indonesian Banknotes Using ROI and CNN,” Proc. - 2021 IEEE Int. Conf. Ind. 4.0, Artif. Intell. Commun. Technol. IAICT 2021, pp. 154–160, 2021, doi: 10.1109/IAICT52856.2021.9532585.

S. Rahmat Fuadi, and Rahmadani, “Performance Comparison Analysis of Classifiers on Binary Classification Dataset,” Indones. J. Data Sci., vol. 4, no. 2, pp. 45-54, 2023, doi: 10.56705/ijodas.v4i2.77.

A. D. Rian Wibisono, Syahrul Hidayat, H. M. Tsubasanofa Ramadhan, and E. Yulia Puspaningrum, “Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification,” Indones. J. Data Sci., vol. 4, no. 2, pp. 87-96, 2023, doi: 10.56705/ijodas.v4i2.70.

X. Zenggang, T. Zhiwen, C. Xiaowen, Z. Xue-min, Z. Kaibin, and Y. Conghuan, “Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features,” J. Signal Process. Syst., vol. 93, no. 2–3, pp. 139–146, 2021, doi: 10.1007/s11265-019-01508-y.

Setiawan, R., and Hayatou Oumarou, “Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques,” Indones. J. Data Sci., vol. 5, no. 1, pp. 54-63, 2023, doi: 10.56705/ijodas.v5i1.129.

E. A. Sekehravani, E. Babulak, and M. Masoodi, “Implementing Canny Edge Detection Algorithm For Noisy Image,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1404–1410, 2020, doi: 10.11591/eei.v9i4.1837.

Setiawan, R., Zein, H., Azdy, R. A., and Sulistyowati, S, “Rice Leaf Disease Classification with Machine Learning: An Approach Using Nu-SVM,” Indones. J. Data Sci., vol. 4, no. 3, pp. 136-144, 2023, doi: 10.56705/ijodas.v4i3.114.

Dima Genemo, M, “Federated Learning for Bronchus Cancer Detection Using Tiny Machine Learning Edge Devices,” Indones. J. Data Sci., vol. 5, no. 1, pp. 64-69, 2024, doi: 10.56705/ijodas.v5i1.116.

Waluyo Poetro, B. S., Maria ⁠., Zein, H., Najwaini, E., and Zulfikar, D. H, “Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification,” Indones. J. Data Sci., vol. 5, no. 1, pp. 15-22, 2024, doi: 10.56705/ijodas.v5i1.123.

Y. W. Kim, J. Innila Rose, and A. V. N. Krishna, “A Study on the Effect of Canny Edge Detection on Downscaled Images,” Pattern Recognit. Image Anal., vol. 30, no. 3, pp. 372–381, 2020, doi: 10.1134/S1054661820030116.

Adi Pratama, I. P., Jullev Atmadji, E. S., Purnamasar, D. A., and Faizal, E, “Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties,” Indones. J. Data Sci., vol. 5 no. 1, pp. 23-29, 2024, doi: 10.56705/ijodas.v5i1.124.

J. Basavaiah and A. Arlene Anthony, “Tomato Leaf Disease Classification using Multiple Feature Extraction Techniques,” Wirel. Pers. Commun., vol. 115, no. 1, pp. 633–651, 2020, doi: 10.1007/s11277-020-07590-x.

X. Ji, H. Guo, and M. Hu, “Features Extraction and Classification of Wood Defect Based on Hu Invariant Moment and Wavelet Moment and BP Neural Network,” Assoc. Comput. Mach. 12th Int. Symp. Vis. Inf. Commun. Interact., 2019, doi: 10.1145/3356422.3356459.

Suhendra, C. D., Najwaini, E., Maria, E., and Faizal, E, “A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel,” Indones. J. Data Sci., vol. 4, no. 3, pp. 151-159, 2023, doi: 10.56705/ijodas.v4i3.112.

S. K. Jadwaa, “X-Ray Lung Image Classification Using a Canny Edge Detector,” J. Electr. Comput. Eng., vol. 2022, 2022, doi: 10.1155/2022/3081584.

J. Yan, Z. Zhang, K. Lin, F. Yang, and X. Luo, “A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks,” Knowledge-Based Syst., vol. 198, p. 105922, 2020, doi: 10.1016/j.knosys.2020.105922.

S. Tangirala, “Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, pp. 612–619, 2020, doi: 10.14569/ijacsa.2020.0110277.

H. Azis and S. R. Jabir, “Chemical Composition and Aroma Profiling: Decision Tree Modeling of Formalin Tofu,” J. Embed. Syst. Secur. Intell. Syst., vol. 04, no. 2, pp. 206–211, 2023.

F. T. Admojo, and Nurul Rismayanti, “Estimating Obesity Levels Using Decision Trees and K-Fold Cross-Validation: A Study on Eating Habits and Physical Conditions,” Indones. J. Data Sci., vol. 5, no. 1, pp. 37-44, 2023, doi: 10.56705/ijodas.v5i1.126.

H. Azis, F. Tangguh Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Agustus, vol. 19, no. 3, pp. 286–294, 2020.

D. Indra, R. Satra, H. Azis, A. R. Manga, and H. L, “Detection System of Strawberry Ripeness Using K-Means,” Ilk. J. Ilm., vol. 14, no. 1, pp. 25–31, 2022, doi: 10.33096/ilkom.v14i1.1054.25-31.

B. Rostami, D. M. Anisuzzaman, C. Wang, S. Gopalakrishnan, J. Niezgoda, and Z. Yu, “Multiclass wound image classification using an ensemble deep CNN-based classifier,” Comput. Biol. Med., vol. 134, no. May, p. 104536, 2021, doi: 10.1016/j.compbiomed.2021.104536.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Huzain Azis, Purnawansyah, Nurul Alfiyyah

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.