Deteksi Dini Anak Disleksia dengan metode Support Vector Machine

 Ardhian Ekawijana (Politeknik Negeri Bandung, Bandung, Indonesia)
 (*)Akhmad Bakhrun Mail (Politeknik Negeri Bandung, Bandung, Indonesia)
 Zulkifli Arsyad (Politeknik Negeri Bandung, Bandung, Indonesia)

(*) Corresponding Author

Submitted: September 1, 2022; Published: September 30, 2022


Dyslexia is a brain disorder caused by genetics. People with dyslexia can live a normal life and even have certain advantages if they get the correct education. People with dyslexia often get the predicate stupid because teachers do not know the case of their students. Early detection of dyslexic children can be done with a series of tests so that the system can conclude that the data is dyslexic or not. Support Vector Machine is a data classification method to share dyslexia test results or not. This system is trained with test results data that are already available using the SVM method. This study uses gamification data to detect dyslexic children or not. SVM proves a good level of accuracy in predictions up to 94%.


Dyslexia; Support Vector Machine; Detection; Classification; Accuracy

Full Text:


Article Metrics

Abstract view : 124 times
PDF - 46 times


S. J. Russell and P. Norvig, Artificial intelligence, vol. 4. Pearson Education, Inc., 2010.

N. Lee, G. Márquez, J. M. Levsky, and J. K. Gohagan, “Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening,” IEEE Rev. Biomed. Eng., vol. 2, no. 2008, pp. 136–146, 2009, doi: 10.1109/RBME.2009.2034022.

Y. Wang et al., “Deeply-Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound,” IEEE Trans. Med. Imaging, vol. 39, no. 4, pp. 866–876, 2020, doi: 10.1109/TMI.2019.2936500.

P. Hidayatullah, T. L. E. R. Mengko, R. Munir, and A. Barlian, “Bull Sperm Tracking and Machine Learning-Based Motility Classification,” IEEE Access, vol. 9, pp. 61159–61170, 2021, doi: 10.1109/ACCESS.2021.3074127.

S. Qi, T. Nie, Q. Li, Z. He, D. Xu, and Q. Chen, “A Sperm Cell Tracking Recognition and Classification Method,” Int. Conf. Syst. Signals, Image Process., vol. 2019-June, pp. 163–167, 2019, doi: 10.1109/IWSSIP.2019.8787312.

R. Kakadiya, R. Lemos, S. Mangalan, M. Pillai, and S. Nikam, “AI Based Automatic Robbery/Theft Detection using Smart Surveillance in Banks,” Proc. 3rd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2019, pp. 201–204, 2019, doi: 10.1109/ICECA.2019.8822186.

F. Fanitabasi and E. Pournaras, “Appliance-Level Flexible Scheduling for Socio-Technical Smart Grid Optimization,” IEEE Access, vol. 8, pp. 119880–119898, 2020, doi: 10.1109/ACCESS.2020.3001763.

J. Han, M. Kamber, and J. Pei, Data mining: Data mining concepts and techniques. 2012.

Loeziana, “Urgensi Mengenal Ciri Disleksia,” J. Pendidik. Kegur., vol. 3, no. 2, pp. 42–58, 2017, [Online]. Available:

S. Shaywitz, “What is Dyslexia?,” The Yale Center For Dyslexia & Creativity, 2022. (accessed Sep. 23, 2022).

L. Rello, R. Baeza-Yates, A. Ali, J. P. Bigham, and M. Serra, “Predicting risk of dyslexia with an online gamified test,” PLoS One, vol. 15, no. 12 December, pp. 1–15, 2020, doi: 10.1371/journal.pone.0241687.

C. Mejia, B. Florian, R. Vatrapu, S. Bull, S. Gomez, and R. Fabregat, “A Novel Web-Based Approach for Visualization and Inspection of Reading Difficulties on University Students,” doi: 10.1109/TLT.2016.2626292.

D. Colenbrander, J. Ricketts, and H. L. Breadmore, “Early identification of dyslexia: Understanding the issues,” Lang. Speech. Hear. Serv. Sch., vol. 49, no. 4, pp. 817–828, 2018, doi: 10.1044/2018_LSHSS-DYSLC-18-0007.

O. L. Usman, R. C. Muniyandi, K. Omar, and M. Mohamad, “Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges,” IEEE Access, vol. 9, pp. 36879–36897, 2021, doi: 10.1109/ACCESS.2021.3062709.

T. S. Kuswiyanti, S. Santoso, and F. Indriyani, “Aplikasi Pengenalan Profesi pada Anak Usia Dini Berbasis Android,” Acad. J. Comput. Sci. Res., vol. 2, no. 2, pp. 2–6, 2020, doi: 10.38101/ajcsr.v2i2.288.

J. Stein, “Why dyslexics make good coders,” Itnow, vol. 60, no. 3, pp. 58–60, 2018, doi: 10.1093/itnow/bwy081.

M. Bernardini, L. Romeo, P. Misericordia, and E. Frontoni, “Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 1, pp. 235–246, 2020, doi: 10.1109/JBHI.2019.2899218.

E. Prasetyo, Data Mining, Mengolah Data Menjadi Informasi Menggunakan Matlab, 1st ed. Yogyakarta: Andi Yogyakarta, 2014.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Deteksi Dini Anak Disleksia dengan metode Support Vector Machine


  • There are currently no refbacks.

Copyright (c) 2022 Ardhian Ekawijana, Akhmad Bakhrun, Zulkifli Arsyad

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

Jurnal Sistem Komputer dan Informatika (JSON)
Dikelola oleh STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

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