Expert System for Diagnosing Learning Disorders in Children Using the Dempster-Shafer Theory Approach
DOI:
10.33395/sinkron.v8i4.12960Keywords:
expert system, Dempster-Shafer theory, learning disorders, level of confidence, probabilityAbstract
Learning disorders can occur in children where a child experiences difficulty mastering important skills such as reading, writing, or arithmetic. Learning disorders can have an emotional impact on children, such as low self-confidence, anxiety, or frustration. Therefore, it is important for parents and educators to recognize the signs of learning disorders so that appropriate intervention can be given. The aim of this research is to develop an expert system that can diagnose learning disorders in children using the Dempster-Shafer Theory algorithm to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach has the ability to provide probability values in evidence based on the level of belief and reasoning in accordance with logic and then combine it with information from certain events. This research produces an expert system built on a website that can diagnose based on symptoms and display diagnosis results, definitions of types of learning disorders, and treatment options. The accuracy test results show a value of 92%, which means that the system built using the Dempster-Shafer Theory approach is able to diagnose learning disorders in children well.
Downloads
References
Ahmad, I., Rahmanto, Y., Pratama, D., & Borman, R. I. (2021). Development of augmented reality application for introducing tangible cultural heritages at the lampung museum using the multimedia development life cycle. ILKOM Jurnal Ilmiah, 13(2), 187–194.
Ahmad, I., Suwarni, E., Borman, R. I., Asmawati, A., Rossi, F., & Jusman, Y. (2022). Implementation of RESTful API Web Services Architecture in Takeaway Application Development. International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 132–137. IEEE. https://doi.org/10.1109/ICE3IS54102.2021.9649679
Andriana, S. D. (2022). Naive Bayes Classifier Method Expert System for Diagnosis of Attention Disorders and Hyperactivity in Children. Indonesian Journal of Education, Social Sciences and Research (IJSSR), 3(3), 145–150. https://doi.org/10.30596/jcositte.v1i1.xxxx
Aro, T., Eklund, K., Eloranta, A.-K., Ahonen, T., & Rescorla, L. (2022). Learning Disabilities Elevate Children’s Risk for Behavioral-Emotional Problems: Differences Between LD Types, Genders, and Contexts. Journal of Learning Disabilities, 55(6), 465–481. https://doi.org/10.1177/00222194211056297
Borman, R. I., Fernando, Y., & Yudoutomo, Y. E. P. (2022). Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(2), 339–345.
Farrell, M. (2021). Supporting Disorders of Learning and Co-ordination: Effective Provision for Dyslexia, Dysgraphia, Dyscalculia, and Dyspraxia. Abingdon: Taylor & Francis.
Fernando, Y., Napianto, R., & Borman, R. I. (2022). Implementasi Algoritma Dempster-Shafer Theory Pada Sistem Pakar Diagnosa Penyakit Psikologis Gangguan Kontrol Impuls. Insearch (Information System Research) Journal, 2(2), 46–54.
Gunawan, R. D., Napianto, R., Borman, R. I., & Hanifah, I. (2019). Implementation of Dijkstra’s Algorithm in Determining the Shortest Path (Case Study: Specialist Doctor Search in Bandar Lampung). International Journal Information System and Computer Science (IJISCS), 3(3), 98–106.
Gupta, I., & Nagpal, G. (2020). Artificial Intelligence and Expert Systems. Mercury Learning and Information.
Haerani, E., Candra, R. M., & Sari, A. A. (2021). Expert System Early Diagnosis of Schizophrenia Using Certainty Factor Methods And Forward Chaining. Seminar Nasional Teknologi Informasi, Komunikasi Dan Industri (SNTIKI), 376–385.
Harjanti, T. W. (2022). Implementation of Inference Engine with Certainty Factor on Potential Diagnosis of Brain Tumor Disease. PILAR Nusa Mandiri: Journal of Computing and Information System, 18(1), 25–30. https://doi.org/10.33480/pilar.v18i1.xxxx
Kariyawasam, R., Nadeeshani, M., Hamid, T., Subasinghe, I., & Ratnayake, P. (2019). A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia. International Conference on Advancements in Computing (ICAC), 156–161. https://doi.org/10.1109/ICAC49085.2019.9103336
Kołodziejczyk, J., Grzegorczyk-Dłuciak, N., & Kuliga, E. (2022). Rule-based expert system supporting Individual Education-and-Therapeutic Program composition in SYSABA. Procedia Computer Science, 207, 4535–4544. https://doi.org/https://doi.org/10.1016/j.procs.2022.09.517
Marlinda, L., Widiyawati, W., Widiastuti, R., & Indrarti, W. (2020). Expert System for Monitoring Elderly Health Using the Certainty Factor Method. SinkrOn, 5(1), 72. https://doi.org/10.33395/sinkron.v5i1.10653
Mayatopani, H., Borman, R. I., Atmojo, W. T., & Arisantoso, A. (2021). Classification of Vehicle Types Using Backpropagation Neural Networks with Metric and Ecentricity Parameters. Jurnal Riset Informatika, 4(1), 65–70. https://doi.org/10.34288/jri.v4i1.293
Napianto, R., Rahmanto, Y., Borman, R. I., Lestari, O., & Nugroho, N. (2021). Dhempster-Shafer Implementation in Overcoming Uncertainty in the Inference Engine for Diagnosing Oral Cavity Cancer. CSRID (Computer Science Research and Its Development Journal), 13(1), 45–53.
Pasaribu, S. A., Sihombing, P., & Suherman, S. (2020). Expert System for Diagnosing Dental and Mouth Diseases with a Website-Based Certainty Factor (CF) Method. MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology, 218–221. https://doi.org/10.1109/MECnIT48290.2020.9166635
Peters, L., & Ansari, D. (2019). Are specific learning disorders truly specific, and are they disorders? Trends in Neuroscience and Education, 17, 100115. https://doi.org/https://doi.org/10.1016/j.tine.2019.100115
Raharjo, T., & Wimbarti, S. (2020). Assessment of learning difficulties in the category of children with dyslexia. Jurnal Konseling Dan Pendidikan, 8(2), 79–85.
Safitri, M., Insani, F., Yanti, N., & Oktavia, L. (2023). Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor. Jurnal Sistem Komputer Dan Informatika (JSON), 4(4), 594–603. https://doi.org/10.30865/json.v4i4.6309
Teguh, D., Fadlil, A., & Sunardi, S. (2019). Sistem Pakar Diagnosa Gangguan Kepribadian Menggunakan Metode Dempster Shafer. Jurnal Sistem Informasi Bisnis, 01, 25–31.
Wanti, L. P., & Puspitasari, L. (2022). Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 16–24. https://doi.org/https://doi.org/10.29207/resti.v6i1.3599
Wen, K., Song, Y., Wu, C., & Li, T. (2020). A Novel Measure of Uncertainty in the Dempster-Shafer Theory. IEEE Access, 8, 51550–51559. https://doi.org/10.1109/ACCESS.2020.2979605
Wijaya, E. (2020). Identification And Intervention of Specific Learning Disorder in Children. Damianus Journal of Medicine, 19(1), 70–79.
Yang, L.-H., Ye, F.-F., Liu, J., & Wang, Y.-M. (2023). Belief rule-base expert system with multilayer tree structure for complex problems modeling. Expert Systems with Applications, 217, 119567. https://doi.org/https://doi.org/10.1016/j.eswa.2023.119567
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2023 Murien Nugraheni, Rini Nuraini, Mursalim Tonggiroh, Siti Nurhayati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
