Determination of the Children Classification with Special Needs in Extraordinary High School in Banjarmasin Using the Naive Bayes

Heru Kartika Candra, Said Muhammad, Rinova Firman Cahyani, Muhammad Bahit, Dodon Turianto Nugrahadi, Billy Sabella


Children with special needs with different physical characteristics are very easy to distinguish. However, in children with special needs with different psychological characteristics, there are difficulties in determining the classification of the child. Children with mental disorders are children who have deviations in the ability to think critically, logically in responding to the world around them. This study determined the classification of children with special needs, the decision support system can analyze using the naive bayes classification (NBC) method. The result showed that Naïve Bayes Classifier algorithm can be used as the basis for the decision-making process by the Special High School Special Education Foundation (SMALB YPLB) Banjarmasin. In this study, it is hoped that using an application based on the Naive Bayesian Classification method can be used as an alternative calculation that is more effective than calculations using the manual method, with the achievement of conformity of the results reaching 80%.


Children with Special Needs; Classification; Naive Bayesian Classification

Full Text:



Hartatik, Purnomo, A., Hartono, R., & Munawaroh, H. (2018). Naïve Bayes Approach for Expert System Design of Children Skin Identification Based on Android. IOP Conference Series: Materials Science and Engineering, 333(1).

Kalcheva, N., & Nikolov, N. (2020). Laplace Naive Bayes classifier in the classification of text in machine learning. 2020 International Conference on Biomedical Innovations and Applications (BIA), 2, 17–19.

Langarizadeh, M., & Moghbeli, F. (2016). Applying naive bayesian networks to disease prediction: A systematic review. Acta Informatica Medica, 24(5), 364–369.

Liu, X., Lu, R., Ma, J., Chen, L., & Qin, B. (2016). Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification. IEEE Journal of Biomedical and Health Informatics, 20(2), 655–668.

Mirza, A. H. (2019). Application of Naive Bayes Classifier Algorithm in Determining New Student Admission Promotion Strategies. Journal of Information Systems and Informatics, 1(1), 14–28.

Putri, T. E., Subagio, R. T., Kusnadi, & Sobiki, P. (2020). Classification System of Toddler Nutrition Status using Naïve Bayes Classifier Based on Z- Score Value and Anthropometry Index. Journal of Physics: Conference Series, 1641(1).

Shinde, T. A., & Prasad, D. J. R. (2017). IoT based Animal Health Monitoring with Naive Bayes Classification. International Journal on Emerging Trends in Technology (IJETT), 1(2), 8104–8107.

Wu, Y. (2018). A New Instance-weighting Naive Bayes Text Classifiers. 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), 198–202.

Yang, F. J. (2018). An implementation of naive bayes classifier. Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, 301–306.

Zulfikar, W. B., Gerhana, Y. A., & Rahmania, A. F. (2018). An Approach to Classify Eligibility Blood Donors Using Decision Tree and Naive Bayes Classifier. 2018 6th International Conference on Cyber and IT Service Management (CITSM), Citsm, 1–5.



  • There are currently no refbacks.

Copyright (c) 2022 International Journal of Multicultural and Multireligious Understanding

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

International Journal of Multicultural and Multireligious Understanding (IJMMU) ISSN 2364-5369
Copyright © 2014-2018 IJMMU. All rights reserved.