BISINDO Sign Letters Recognition Through HOG Features and Bagging Decision Tree

Dolly Indra; Erick Irawadi Alwi; Faudiah Anwar; St. Nadya Kurnia Prihandani

doi:10.35760/ik.2025.v30i3.57

Penulis

Dolly Indra Universitas Muslim Indonesia Penulis
Erick Irawadi Alwi Universitas Muslim Indonesia Penulis
Faudiah Anwar Universitas Muslim Indonesia Penulis
St. Nadya Kurnia Prihandani Universitas Muslim Indonesia Penulis

DOI:

https://doi.org/10.35760/ik.2025.v30i3.57

Kata Kunci:

BISINDO, Grayscale, Median Filter, HOG, Bagging Decision Tree

Abstrak

Sign language is one of the primary means of communication for people with hearing disabilities. BISINDO (Indonesian sign language) communicates using hand movements, among other things. One solution to this problem is to use image processing to recognize BISINDO letters A-Z based on hand movements. This study aims to create a BISINDO letter recognition system based on image processing using several stages, namely, preprocessing such as converting RGB images to grayscale images, then improving image quality by adjusting image contrast and removing noise with a median filter, HOG (Histogram of Oriented Gradients) feature extraction, and Bagging Decision Tree classification. A total of 156 images were used in the dataset, consisting of 104 letter images for training data and 52 letter images for test data. The data will be processed in the system as training data, and the dataset will then be stored in ‘mat’ format. Based on the results of testing Classification using Bagging Decision Tree, which produced an average accuracy rate of 86.5%. Thus, this research is expected to contribute to the development of BISINDO character recognition technology based on digital image processing.

Referensi

[1] T. Handhika, R. I. M. Zen, D. P. Lestari, and I. Sari, “Gesture recognition for Indonesian sign language (BISINDO),” in Journal of Physics: Conference Series, 2018, vol. 1028, no. 1, p. 12173.

[2] L. N. Hayati, A. N. Handayani, W. S. G. Irianto, R. A. Asmara, D. Indra, and N. S. Damanhuri, “Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems,” Int. J. Eng. Sci. Inf. Technol., vol. 5, no. 3, pp. 285–294, 2025, doi: 10.52088/ijesty.v5i3.948.

[3] A. F. Deleviar, Intan Oktaviani, and Hanifah Permatasari, “Pengembangan Website Speech To Video Bahasa Isyarat Indonesia (Bisindo) Berbasis Algoritma Long Shot Term Memory,” Infotek J. Inform. dan Teknol., vol. 8, no. 1, pp. 23–33, 2025, doi: 10.29408/jit.v8i1.26117.

[4] A. Alayed, “Machine Learning and Deep Learning Approaches for Arabic Sign Language Recognition: A Decade Systematic Literature Review,” Sensors, vol. 24, no. 23, 2024, doi: 10.3390/s24237798.

[5] F. M. Najib, “Sign language interpretation using machine learning and artificial intelligence,” Neural Comput. Appl., vol. 37, no. 2, pp. 841–857, 2025, doi: 10.1007/s00521-024-10395-9.

[6] N. Nurhadi, E. A. Winanto, R. M. Said, J. Jasmir, and L. Afuan, “Pattern Classification Sign Language Using Features Descriptors And Machine Learning,” J. Tek. Inform., vol. 5, no. 2, pp. 349–356, 2024.

[7] S. Tan, N. Khan, Z. An, Y. Ando, R. Kawakami, and K. Nakadai, “A review of deep learning-based approaches to sign language processing,” Adv. Robot., vol. 38, no. 23, pp. 1649–1667, 2024, doi: 10.1080/01691864.2024.2442721.

[8] R. Goel, S. Bansal, and K. Gupta, “Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization,” Sci. Rep., vol. 15, no. 1, p. 2300, 2025, doi: 10.1038/s41598-024-82785-x.

[9] F. Farhana, A. Rezha, E. Najaf, and R. Permatasari, “BISINDO Sign Language Interpreter System Using YOLOv8 and CNN,” Bit-Tech (Binary Digit. - Technol., vol. 8, no. 1, 2025, doi: 10.32877/bt.v8i1.2638.

[10] D. Indra, L. N. Hayati, M. A. Daris, I. As’ad, and U. Mansyur, “Penerapan Metode Random Forest Dalam Klasifikasi Huruf BISINDO dengan Menggunakan Ekstraksi Fitur Warna dan Bentuk The Application of Random Forest Method in BISINDO Letter Classification Using Color and Shape Feature Extraction .,” Komputika J. Sist. Komput., vol. 9, no. 28, 2024, doi: 10.34010/komputika.v13i1.10363.

[11] D. Indra, L. N. Hayati, and M. C. Irja, “Pengenalan Huruf BISINDO Menggunakan Chain Code Contour dan Naive Bayes BISINDO letter recognition using Chain Code Contour and Naive Bayes,” Komputika J. Sist. Komput., vol. 13, no. 148, pp. 11–18, 2024, doi: 10.34010/komputika.v13i1.10360.

[12] A. D. Goenawan and S. Hartati, “The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time,” Sci. J. Informatics, vol. 11, no. 1, pp. 237–244, 2024, doi: 10.15294/sji.v11i1.48475.

[13] G. Joshi, S. Singh, and R. Vig, “Taguchi-TOPSIS based HOG parameter selection for complex background sign language recognition,” J. Vis. Commun. Image Represent., vol. 71, p. 102834, 2020, doi: https://doi.org/10.1016/j.jvcir.2020.102834.

[14] F. Jafari and A. Basu, “Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning,” Sensors, vol. 23, no. 18, pp. 1–15, 2023, doi: 10.3390/s23187790.

[15] P. Yariyan et al., “Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping,” Water Resour. Manag., vol. 34, no. 9, pp. 3037–3053, 2020.

[16] S. Boukir and W. Feng, “Boundary bagging to address training data issues in ensemble classification,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 9975–9981.

[17] A. Padmo, A. Masa, M. Mushfa, H. Fajri, A. Septiarini, and E. Winarno, “Comparison of Noise Using Reduction Method for Repairing Digital Image,” JOIV Int. J. Informatics Vis., vol. 8, no. December, pp. 2189–2194, 2024, [Online]. Available: www.joiv.org/index.php/joiv.

[18] N. T. Singh, C. Kaur, A. Chaudhary, and S. Goyal, “Preprocessing of Medical Images using Deep Learning: A Comprehensive Review,” Proc. 2023 2nd Int. Conf. Augment. Intell. Sustain. Syst. ICAISS 2023, no. August, pp. 521–527, 2023, doi: 10.1109/ICAISS58487.2023.10250462.

[19] S. Bhave, A. Giri, S. Bhavsar, and G. Chiddarwar, “Effective method for Shape based Image Retrieval using Pyramid of Histogram of Oriented Gradients,” in 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), 2019, pp. 1–4.

[20] Y. Pang, Y. Yuan, X. Li, and J. Pan, “Efficient HOG human detection,” Signal Processing, vol. 91, no. 4, pp. 773–781, 2011.

[21] M. Azad, T. H. Nehal, and M. Moshkov, “A novel ensemble learning method using majority based voting of multiple selective decision trees,” Computing, vol. 107, no. 1, p. 42, 2025.