Penerapan Jaringan Saraf Buatan untuk Pengenalan Pola Tanda Tangan dalam Identifikasi Potensial Diri Menggunakan Metode Backpropagation
DOI:
https://doi.org/10.62383/polygon.v4i1.892Keywords:
Artificial Neural Networks, Backpropagation, PCA, Self-Potential Identification, Signature RecognitionAbstract
This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.
Downloads
References
Abdirahma, A. A., Hashi, A. O., Elmi, M. A., & Rodriguez, O. E. R. (2024). Advancing handwritten signature verification through deep learning: A comprehensive study and high-precision approach. International Journal of Engineering Trends and Technology, 72(4), 81–91. https://doi.org/10.14445/22315381/IJETT-V72I4P109
Ali Abdulrahman Safar, A., Salih, D. M., & Murshid, A. M. (2023). Pattern recognition using the multi-layer perceptron (MLP) for medical disease: A survey. International Journal of Nonlinear Analysis and Applications, 14(1), 1989–1998.
Amalia, R., Bimantoro, F., & Aranta, A. (2022). Penerapan metode backpropagation dan image centroid zone–zone centroid zone pada pengenalan pola tulisan tangan aksara Bima (Implementation of backpropagation and ICZ–ZCZ for handwriting recognition of Bima script). JTIKA, 4(1), 88–96. http://jtika.if.unram.ac.id/index.php/JTIKA/
Deore, S. P. (2022). Human behavior identification based on graphology using artificial neural network. Acadlore Transactions on AI and Machine Learning, 1(2), 101–108. https://doi.org/10.56578/ataiml010204
Ekwonwune, E. N., Ekekwe, D. A., Ubochi, C. I., & Oleribe, H. C. (2024). Dynamic signature verification using pattern recognition. Journal of Software Engineering and Applications, 17(5), 214–227. https://doi.org/10.4236/jsea.2024.175012
Gibran, M. K. (2025). Penerapan teknik binarisasi dan segmentasi. Jurnal Minfo Polgan, 14, 623–630.
Harizahayu. (2021). Facial emotion recognition based on artificial neural network backpropagation with principal component analysis method. Barekeng, 15(1), 37–46. https://doi.org/10.30598/barekengvol15iss1pp037-046
Hasibuan, L. M., Fauzi, A., & Simanjuntak, M. (2022). Signature recognition using backpropagation artificial neural network method. International Journal of Health Engineering and Technology, 1(2), 63–70. https://doi.org/10.55227/ijhet.v1i2.18
Ihsan, M., Harahap, L. S., & Raya, F. H. (2025). Analysis of pattern recognition methods in digital image processing: A review of recent literature. PKM-P, 9(1), 171–175. https://doi.org/10.32832/jurma.v9i1.2695
Kurniawan, D., & Sunandar, H. (2024). Pengenalan tulisan tangan menggunakan jaringan saraf tiruan dengan algoritma perceptron. Pendas, 9(4), 294–299. https://journal.unpas.ac.id/index.php/pendas/article/view/17918/9088
Liu, R., & Xin, Y. (2023). Online handwritten signature verification method based on uni-feature correlation coefficient between signatures. Sensors, 23(23). https://doi.org/10.3390/s23239341
Narwade, D., Pawale, O., More, M., & Shelar, S. (2025). Dynamic forgery signature detection using CNN and PCA. (Informasi jurnal belum tersedia).
Roszczewska, K., & Niewiadomska-Szynkiewicz, E. (2024). Online signature biometrics for mobile devices. Sensors, 24(11). https://doi.org/10.3390/s24113524
Samsuryadi, Kurniawan, R., Supardi, J., Sukemi, & Mohamad, F. S. (2023). A framework for determining the Big Five personality traits using machine learning classification through graphology. Journal of Electrical and Computer Engineering, 2023. https://doi.org/10.1155/2023/1249004
Sanjaya, R., Abdullah, A., & Usman, U. (2022). Penerapan metode multilayer perceptron untuk memprediksi tanda tangan mahasiswa. Jurnal Algoritma, Logika dan Komputasi, 5(2). https://doi.org/10.30813/j-alu.v5i02.2760
Saputra, M. A., & Nurhaida, I. (2024). Signature originality verification using a deep learning approach. Electronic Journal of Education, Social Economics and Technology, 5(1), 19–29. https://doi.org/10.33122/ejeset.v5i1.310
Shah, D., Patni, A., Shirahatti, V., Vyas, U., & Kulkarni, D. (2025). Deep learning-based signature authentication using Siamese CNN. International Journal on Science and Technology, 16(2), 1–9. https://doi.org/10.71097/ijsat.v16.i2.3502
Supriyanto, Sunardi, & Riadi, I. (2022). Pengaruh nilai hidden layer dan learning rate terhadap kecepatan pelatihan jaringan saraf tiruan backpropagation. JIKO (Jurnal Informatika dan Komputer), 6(1), 27. https://doi.org/10.26798/jiko.v6i1.508
Tahir, A. (2025). Performance analysis of neural networks with backpropagation on binary and multi-class data classification. [Nama jurnal tidak tersedia], 10(1), 77–83.
Wright, L. G., Onodera, T., Stein, M. M., Wang, T., Schachter, D. T., Hu, Z., & McMahon, P. L. (2022). Deep physical neural networks trained with backpropagation. Nature, 601(7894), 549–555. https://doi.org/10.1038/s41586-021-04223-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
