Deteksi Dini Anak Disleksia dengan metode Support Vector Machine

Authors

  • Ardhian Ekawijana Politeknik Negeri Bandung, Bandung
  • Akhmad Bakhrun Politeknik Negeri Bandung, Bandung
  • Zulkifli Arsyad Politeknik Negeri Bandung, Bandung

DOI:

https://doi.org/10.30865/json.v4i1.4776

Keywords:

Dyslexia, Support Vector Machine, Detection, Classification, Accuracy

Abstract

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%.

Author Biography

Ardhian Ekawijana, Politeknik Negeri Bandung, Bandung

Jurusan Teknik Komputer dan Informatika

References

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: http://jurnal.ar-raniry.ac.id/index.php/bunayya/article/download/1698/1235.

S. Shaywitz, “What is Dyslexia?,†The Yale Center For Dyslexia & Creativity, 2022. https://dyslexia.yale.edu/dyslexia/what-is-dyslexia/ (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.

Downloads

Published

2022-09-30

How to Cite

Ekawijana, A., Bakhrun, A., & Arsyad, Z. (2022). Deteksi Dini Anak Disleksia dengan metode Support Vector Machine. Jurnal Sistem Komputer Dan Informatika (JSON), 4(1), 217–224. https://doi.org/10.30865/json.v4i1.4776