Comparative Performance Evaluation of Classification Methods for Arabic Numeral Handwritten Recognition

Intan Novita Saly, Purnawansyah Purnawansyah, Huzain Azis

Abstract

This study aims to evaluate the performance of various classification methods in recognizing handwritten Arabic numerals, particularly the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and NU Support Vector Classifier (NU SVC) algorithms. In this study, a dataset of handwritten Arabic numerals consisting of 9,350 samples with 10 different classes was used. The research process involved data collection, data labeling, dividing the dataset into training and testing data, implementing classification algorithms, and performance testing using cross-validation methods. The results showed that NU SVC had more stable performance with accuracy close to KNN, while GNB showed the lowest performance. The conclusion of this study emphasizes that the selection of algorithms and parameter optimization is crucial to improve the accuracy and efficiency of handwriting recognition systems. Support Vector Machine (SVM) based algorithms proved to be superior in handling complex classification tasks compared to GNB. This study provides significant contributions to the field of handwriting recognition, particularly in the context of Arabic numeral handwriting, and can serve as a reference for developers of optical character recognition (OCR) systems in the future. Future research is recommended to increase the variety of datasets and further explore parameter optimization and data preprocessing techniques to improve system accuracy.

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References

A. El-Sawy, M. Loey, and H. El-Bakry, “Arabic Handwritten Characters Recognition using Convolutional Neural Network,” 2019 10th International Conference on Information and Communication Systems, ICICS 2019, vol. 5, pp. 147–151, 2017, doi: 10.1109/IACS.2019.8809122.

N. Bonneton-Botté, L. Miramand, R. Bailly, and C. Pons, “Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/children10071096.

F. T. Admojo and Y. I. Sulistya, “Analisis performa algoritma Stochastic Gradient Descent (SGD) dalam mengklasifikasi tahu berformalin,” Indonesian Journal of Data and Science (IJODAS), vol. 3, no. 1, 2022.

A. A. SG Mas Karunia Maharani, K. Oka Saputra, and N. Made Ary Esta Dewi Wirastuti, “Comparison of Backpropagation Neural Network and Convolutional Neural Network on Handwriting Pattern Recognition”. [Online]. Available: http://jcosine.if.unram.ac.id/

M. Wibowo, M. Rizieq, and F. Djafar, “Jurnal Media Informatika Budidarma Perbandingan Metode Klasifikasi Untuk Deteksi Stress Pada Mahasiswa di Perguruan Tinggi,” 2023, doi: 10.30865/mib.v7i1.5182.

N. Altwaijry and I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Comput Appl, vol. 33, no. 7, pp. 2249–2261, Apr. 2021, doi: 10.1007/s00521-020-05070-8.

N. Alrobah and S. Albahli, “Arabic handwritten recognition using deep learning: A survey,” Arab J Sci Eng, 2022, doi: 10.1007/s13369-021-06363-3.

J. Shin et al., “Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods,” Applied Sciences (Switzerland), vol. 12, no. 10, May 2022, doi: 10.3390/app12105256.

S. Syaifudin, D. Pratiwi, T. Rahardiansyah, “Handwriting prediction using the support vector machine method in web-based applications,” AIP Conference, 2022, [Online]. Available: https://pubs.aip.org/aip/acp/article-abstract/2659/1/090004/2831896

S. Kreatindo Manokwari, N. Yarkuran, I. Teknologi dan Bisnis Muhammadiyah Wakatobi, and F. Aziz, “Klasifikasi Kategori Obat Menggunakan Algoritma Support Vector Machine Drug Category Classification Using Support Vector Machine Algorithm,” JANUARI, vol. 1, no. 1, p. 2023, 2023, [Online]. Available: https://www.kaggle.com/prathamtripathi/drug-

N. Čerkez and V. Vareškić, “Machine Learning Approaches to Personality Classification on Imbalanced MBTI Datasets,” 2021 44th International Convention, 2021, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9596742/

H. Azis and I. N. Saly, “Handwritten Arabic Numerals (0-9) Image Dataset,” 2024, Mendeley Data.

H. Azis, N. Alfiyyah, and ; Purnawansyah, “Multiclass Classification on Nominal Value of Rupiah Banknotes Based on Image Processing,” ILKOM Scientific Journal, vol. 16, no. 01, 2024, doi: 10.33096/ilkom.v1.

H. Oumarou and N. Rismayanti, “Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm,” Indonesian Journal of Data and Science, vol. 4, 2023, doi: 10.56705/ijodas.

W. Ye, Y. Xia, and Q. Wang, “An Improved Canny Algorithm for Edge Detection,” Journal of Computational Information Systems, vol. 75, pp. 1516–1523, 2011, doi: 10.1109/WCSE.2009.718.

S. Niu, J. Yang, S. Wang, and G. Chen, “Improvement and parallel implementation of canny edge detection algorithm based on GPU,” Proceedings of International Conference on ASIC, no. 6, pp. 641–644, 2011, doi: 10.1109/ASICON.2011.6157287.

A. Sinra and Husni Angriani, “Automated Classification of COVID-19 Chest X-ray Images Using Ensemble Machine Learning Methods,” Indonesian Journal of Data and Science, vol. 5, no. 1, pp. 45–53, Mar. 2024, doi: 10.56705/ijodas.v5i1.127.

Z. Huang and J. Leng, “Analysis of Hu’s moment invariants on image scaling and rotation,” in Proc. of 2nd International Conference on Computer Engineering and Technology (ICCET, 2010, pp. V7-476. doi: 10.1109/ICCET.2010.5485542.

M. M. Baharuddin, T. Hasanuddin, and H. Azis, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm, vol. 11, pp. 269–274, 2019.

Y. Yang, H. G. Yeh, W. Zhang, C. J. Lee, and ..., “Feature extraction, selection, and K-nearest neighbors algorithm for shark behavior classification based on imbalanced dataset,” IEEE Sensors, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9261357/

S. Ferani et al., “Klasifikasi Ulasan Aplikasi TikTok Menggunakan Algoritma K-Nearest Neighbor dan Chi Square.”

A. Huang, R. Xu, Y. Chen, and M. Guo, “Research on multi-label user classification of social media based on ML-KNN algorithm,” Technol Forecast Soc Change, vol. 188, Mar. 2023, doi: 10.1016/j.techfore.2022.122271.

K. Veena, K. Meena, Y. Teekaraman, R. Kuppusamy, and A. Radhakrishnan, “C SVM Classification and KNN Techniques for Cyber Crime Detection,” Wirel Commun Mob Comput, vol. 2022, 2022, doi: 10.1155/2022/3640017.

A. Nurul, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” Indonesian Journal of Data and Science, vol. 3, no. 3, pp. 115–121, 2022.

Herman et al., “Comparison of Artificial Neural Network and Gaussian Naïve Bayes in Recognition of Hand-Writing Number,” in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2018, pp. 276–279. doi: 10.1109/EIConCIT.2018.8878651.

A. Fitria and H. Azis, “Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier,” Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi, vol. 3, no. 2, pp. 102–106, 2018.

S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indonesian Journal of Data and Science, vol. 1, no. 3, pp. 79–86, 2020, doi: 10.33096/ijodas.v1i3.20.

St. F. Fattah and Purnawansyah, “Analisis sentimen terhadap Body Shaming pada Twitter menggunakan Metode Naïve Bayes Classifier,” Indonesian Journal of Data and Science, vol. 3, no. 2, pp. 61–71, 2022, doi: 10.56705/ijodas.v3i2.46.

DOI: https://doi.org/10.37058/innovatics.v6i2.11998

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