Image Classification pada Kasus American Sign Language Menggunakan Support Vector Machine


  • Zulkifli Universitas Almuslim, Bireuen, Indonesia
  • Imam Muslem R Universitas Almuslim, Bireuen, Indonesia



Hand Image Classification; American Sign Language; Support Vector Machine; Image Processing; Digit Identification


This study aims to develop and test a hand drawing classification model using the Support Vector Machine (SVM) algorithm to identify digits in American Sign Language (ASL). This method utilizes image processing techniques for extracting relevant features from hand images and SVM's ability to separate complex patterns. The training and test data consists of hand images representing the digits 0 through 9 in ASL. Tests are performed using test data that the model has never seen during training, to measure the performance and validity of the model in real-world situations. The results showed that the developed classification model was able to recognize digits in ASL with satisfactory accuracy, where the accuracy of the developed model was 99.8% with a loss of 0.018. Error analysis provides insight into situations that confuse the model and the potential for further improvement. The use of SVM in this ASL classification opens up new opportunities in strengthening communication accessibility for the hand sign language community. In conclusion, this model has the potential to make a positive contribution in facilitating communication and inclusion for deaf communities.


Download data is not yet available.


I. Muslem R, “Sistem Pendeteksi Kebocoran Gas Rumah Tangga Menggunakan Mq-2 Sensor Dan Mikrokontroler,” JURNAL TIKA, vol. 6, no. 02, 2021, doi: 10.51179/tika.v6i02.457.

I. M. R, “Analisis Metode AHP (Analytical Hierarchy Process) Berdasarkan Nilai Consistency Ratio,” 2014.

T. Fakhrurrazi, T. M. Johan, and I. Muslem R., “Rancang Bangun Sistem Pengamanan Pintu Rumah Menggunakan Android Berbasis Arduino Uno,” Jurnal Ilmu Komputer Aceh, vol. 1, no. 1, 2023, doi: 10.51179/ilka.v1i1.1896.

D. Armiady and I. Muslem R., “Penetapan Klaster Siswa Unggul Dengan Menggunakan Algoritma Roc-Smarter,” Jurnal TIKA, vol. 7, no. 2, 2022, doi: 10.51179/tika.v7i2.1229.

I. Muslem, “Prototype Kunci RFID (Radio Frequency Identification) dalam Meningkatkan Keamanan Kendaraan Bermotor,” JURNAL TIKA, vol. 5, no. 3, 2021, doi: 10.51179/tika.v5i3.104.

R. Dewi, T. M. Johan, and I. Muslem R., “Aplikasi Kriptografi Dalam Mengamankan Pesan Teks Dengan Metode Algoritma Rc4 Berbasis Android,” JURNAL TIKA, vol. 6, no. 01, 2021, doi: 10.51179/tika.v6i01.416.

T. Firmansyah and I. M. R, “Visualisasi Instruksi Kerja Sistem Informasi Pelelangan Online E-Auction Pt Pupuk Iskandar Muda Berbasis Multimedia,” Tika, vol. 4, no. 1, 2019.

F. Fitriani and I. Muslem R, “E-Absensi Mahasiswa Fakultas Ilmu Komputer Universitas Almuslim Berbasis Web,” JURNAL TIKA, vol. 5, no. 3, 2021, doi: 10.51179/tika.v5i3.141.

S. Winar, E. Rizki Putra, and I. Muslem R., “Sistem Informasi Kalkulasi Zakat Pada Kantor Baitul Mal Kabupaten Bireuen Berbasis Android,” Jurnal TIKA, vol. 7, no. 3, 2022, doi: 10.51179/tika.v7i3.1584.

R. Dewi, T. M. Johan, and I. Muslem R., “Aplikasi Kriptografi Dalam Mengamankan Pesan Teks Dengan Metode Algoritma Rc4 Berbasis Android,” JURNAL TIKA, vol. 6, no. 01, 2021, doi: 10.51179/tika.v6i01.416.

J. E. (Hans) Korteling, G. C. van de Boer-Visschedijk, R. A. M. Blankendaal, R. C. Boonekamp, and A. R. Eikelboom, “Human- versus Artificial Intelligence,” Front Artif Intell, vol. 4, 2021, doi: 10.3389/frai.2021.622364.

S. Thiebes, S. Lins, and A. Sunyaev, “Trustworthy artificial intelligence,” Electronic Markets, vol. 31, no. 2, 2021, doi: 10.1007/s12525-020-00441-4.

Nasri, “Kecerdasan buatan ( Artificial Intelligence ),” Artif Intell, vol. 1, no. 2, 2014.

W. L. Chen et al., “AgriTalk: IoT for precision soil farming of turmeric cultivation,” IEEE Internet Things J, vol. 6, no. 3, 2019, doi: 10.1109/JIOT.2019.2899128.

Z. Zulkifli, “Sistem Pendeteksi Penyakit Tanaman Padi Berbasis Artificial Intelligence,” JURNAL TIKA, vol. 6, no. 03, 2021, doi: 10.51179/tika.v6i03.813.

C. Domínguez, J. Heras, and V. Pascual, “IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine,” Comput Biol Med, vol. 84, 2017, doi: 10.1016/j.compbiomed.2017.03.027.

Z. Abdellah et al., “Finishing the euchromatic sequence of the human genome,” Nature, vol. 431, no. 7011, 2004, doi: 10.1038/nature03001.

Y. Ruan, X. Xue, and Y. Shen, “Quantum Image Processing: Opportunities and Challenges,” Mathematical Problems in Engineering, vol. 2021. 2021. doi: 10.1155/2021/6671613.

N. Caselli, C. Occhino, B. Artacho, A. Savakis, and M. Dye, “Perceptual optimization of language: Evidence from American Sign Language,” Cognition, vol. 224, 2022, doi: 10.1016/j.cognition.2022.105040.

L. Hou, “LOOKing for multi-word expressions in American Sign Language,” Cogn Linguist, vol. 33, no. 2, 2022, doi: 10.1515/cog-2020-0086.

S. Alshomrani, L. Aljoudi, and M. Arif, “Arabic and American Sign Languages Alphabet Recognition by Convolutional Neural Network,” Advances in Science and Technology Research Journal, vol. 15, no. 4, 2021, doi: 10.12913/22998624/142012.

J. Lee, Y. Susik, and B. S. Lee, “Real-time outlier detection method and apparatus in multidimensional data stream,” US Patent App. 17/354,219, 2022, [Online]. Available:

Y. Gu, R. K. Ganesan, B. Bischke, and ..., “Grid-based outlier detection in large data sets for combine harvesters,” 2017 IEEE 15th …, 2017, [Online]. Available:

S. Z. Gurbuz et al., “American Sign Language Recognition Using RF Sensing,” IEEE Sens J, vol. 21, no. 3, 2021, doi: 10.1109/JSEN.2020.3022376.

M. E. McGarry, K. J. Midgley, P. J. Holcomb, and K. Emmorey, “How (and why) does iconicity effect lexical access: An electrophysiological study of American sign language,” Neuropsychologia, vol. 183, 2023, doi: 10.1016/j.neuropsychologia.2023.108516.

Z. S. Sehyr and K. Emmorey, “The effects of multiple linguistic variables on picture naming in American Sign Language,” Behav Res Methods, vol. 54, no. 5, 2022, doi: 10.3758/s13428-021-01751-x.

B. Thompson, M. Perlman, G. Lupyan, Z. E. D. Sevcikova Sehyr, and K. Emmorey, “A data-driven approach to the semantics of iconicity in American Sign Language and English,” Lang Cogn, vol. 12, no. 1, 2020, doi: 10.1017/langcog.2019.52.

Q. Shao et al., “Teaching American Sign Language in Mixed Reality,” Proc ACM Interact Mob Wearable Ubiquitous Technol, vol. 4, no. 4, 2020, doi: 10.1145/3432211.

D. R. Thompson, D. Zurakowski, and ..., “Endoscopic versus open repair for craniosynostosis in infants using propensity score matching to compare outcomes: a multicenter study from the Pediatric …,” Anesthesia & …, 2018, [Online]. Available:

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min Knowl Discov, vol. 2, no. 2, 1998, doi: 10.1023/A:1009715923555.

C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach Learn, vol. 20, no. 3, 1995, doi: 10.1023/A:1022627411411.

X. Huang, “Computer-implemented method for detecting fraudulent transactions using locality sensitive hashing and locality outlier factor algorithms,” US Patent 11,263,643, 2022, [Online]. Available:

M. Khodagholi, A. Dolati, and ..., “A New Method to Determine Data Membership and Find Noise and Outlier Data Using Fuzzy Support Vector Machine,” Signal and Data …, 2018, [Online]. Available:

A. Rizwan, N. Iqbal, R. Ahmad, and D. H. Kim, “Wr-svm model based on the margin radius approach for solving the minimum enclosing ball problem in support vector machine classification,” Applied Sciences (Switzerland), vol. 11, no. 10, 2021, doi: 10.3390/app11104657.

R. Umar, I. Riadi, and D. A. Faroek, “A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM),” Journal of Applied Informatics and Computing, vol. 4, no. 2, 2020, doi: 10.30871/jaic.v4i2.2226.

W. Huang et al., “Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM,” Appl Soft Comput, vol. 109, 2021, doi: 10.1016/j.asoc.2021.107541.

V. K. Chauhan, K. Dahiya, and A. Sharma, “Problem formulations and solvers in linear SVM: a review,” Artificial Intelligence Review, vol. 52, no. 2. 2019. doi: 10.1007/s10462-018-9614-6.

M. W. Huang, C. W. Chen, W. C. Lin, S. W. Ke, and C. F. Tsai, “SVM and SVM ensembles in breast cancer prediction,” PLoS One, vol. 12, no. 1, 2017, doi: 10.1371/journal.pone.0161501.

M. F. Naufal, “Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 2, 2021, doi: 10.25126/jtiik.2021824553.

M. A. Chandra and S. S. Bedi, “Survey on SVM and their application in image classification,” International Journal of Information Technology (Singapore), vol. 13, no. 5, 2021, doi: 10.1007/s41870-017-0080-1.

L. K. Ramasamy, S. Kadry, Y. Nam, and M. N. Meqdad, “Performance analysis of sentiments in Twitter dataset using SVM models,” International Journal of Electrical and Computer Engineering, vol. 11, no. 3, 2021, doi: 10.11591/ijece.v11i3.pp2275-2284.

B. Ma, L. Yuan, S. Xu, K. Zheng, F. Huang, and ..., “Positive Active Power Outlier Detection based on One-Class SVM,” 2020 12th IEEE PES …, 2020, [Online]. Available:

Z. Ma, S. Lu, R. Zhang, H. Yu, and ..., “Outlier Detection Method of Three Rate Value Based on One Class SVM,” 2021 36th Youth Academic …, 2021, [Online]. Available:

Y. E. Yana and N. Nafi’iyah, “Klasifikasi Jenis Pisang Berdasarkan Fitur Warna, Tekstur, Bentuk Citra Menggunakan SVM dan KNN,” RESEARCH?: Journal of Computer, Information System & Technology Management, vol. 4, no. 1, 2021, doi: 10.25273/research.v4i1.6687.

T. Tang, S. Chen, M. Zhao, W. Huang, and J. Luo, “Very large-scale data classification based on K-means clustering and multi-kernel SVM,” Soft comput, 2019, doi: 10.1007/s00500-018-3041-0.

X. Huang, A. Maier, J. Hornegger, and J. A. K. Suykens, “Indefinite kernels in least squares support vector machines and principal component analysis,” Appl Comput Harmon Anal, vol. 43, no. 1, 2017, doi: 10.1016/j.acha.2016.09.001.

C. Tao, T. Li, and J. Huang, “Kernel choice in one-class support vector machines for novelty and outlier detection,” … on Machine Learning, Big Data and …, 2020, [Online]. Available:

M. Sarmad and M. Mohammadi, “Outlier detection for support vector machine using minimum covariance determinant estimator,” Journal of Artificial Intelligence and …, 2018, [Online]. Available:

R. G. Negri, S. J. S. Sant’Anna, and L. V. Dutra, “A new contextual version of Support Vector Machine based on hyperplane translation,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2013. doi: 10.1109/IGARSS.2013.6723486.

N. Rahmansyah, “Analisa Algoritma Support Vector Machine (Svm) Dalam Memprediksi Nasabah Yang Berpeluang Kredit Macet,” Jurnal KomTekInfo, vol. 3, no. 1, 2016.

S. Wang, Q. Liu, E. Zhu, F. Porikli, and J. Yin, “Hyperparameter selection of one-class support vector machine by self-adaptive data shifting,” Pattern Recognit, 2018, [Online]. Available:

G. Bin Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, 2012, doi: 10.1109/TSMCB.2011.2168604.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, “Stacked denoising autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion,” Journal of Machine Learning Research, vol. 11, 2010.

M. E. Tipping, “Sparse Bayesian Learning and the Relevance Vector Machine,” Journal of Machine Learning Research, vol. 1, no. 3, 2001.

C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans Neural Netw, vol. 13, no. 2, 2002, doi: 10.1109/72.991427.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans Pattern Anal Mach Intell, vol. 32, no. 9, 2010, doi: 10.1109/TPAMI.2009.167.

J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Process Lett, vol. 9, no. 3, 1999, doi: 10.1023/A:1018628609742.

C. C. Chang and C. J. Lin, “LIBSVM: A Library for support vector machines,” ACM Trans Intell Syst Technol, vol. 2, no. 3, 2011, doi: 10.1145/1961189.1961199.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Image Classification pada Kasus American Sign Language Menggunakan Support Vector Machine

Dimensions Badge


Published: 2023-10-31
Abstract View: 14 times
PDF Download: 2 times