Implementation of The Random Forest Algorithm for Early Detection Indications of Autism in Special Needs School (SLB) Students

Article Sidebar

PDF
Published: 2026-06-20
DOI: https://doi.org/10.70491/tifda.v3i1.143
Keywords:
Autism, Random Forest, Prediction, Special Needs School, AQ-10 Questionnaire, Web-Based System, Machine Learning

Main Article Content

Bagus Tri Mahardika
Darma Persada University
Duha Nur Pambudi

Abstract

This study aims to develop a system for early detection signs of autism in pupils at Special Needs Schools (SLB) by applying the Random Forest method. The problem addressed is how to provide an accurate and easily accessible tool for the early identification of signs of autism. The solution involves developing a Random Forest-based classification model using data from the Autism Spectrum Quotient (AQ-10) questionnaire, and then integrating it into a web application system built with a PHP frontend and a Flask backend. This system allows users to complete the questionnaire, upload data, and obtain prediction results automatically. Test results show that the model has an average accuracy of 99%, precision of 98%, recall of 100%, and an F1-score of 99%, as well as an AUC value above 0.98 in every fold. Consequently, this system is effective as a tool for initial screening to detect signs of autism in students at special schools in a practical and efficient manner.

Article Details

Issue

Vol. 3 No. 1 (2026): Journal Technology Information and Data Analytic

Section

Articles
Creative Commons License

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

https://creativecommons.org/licenses/by/4.0/

How to Cite

Implementation of The Random Forest Algorithm for Early Detection Indications of Autism in Special Needs School (SLB) Students. (2026). Journal Technology Information and Data Analytic, 3(1), 19-23. https://doi.org/10.70491/tifda.v3i1.143

References

[1] I. Sopiandi, R. Waliyudin Hidayat, and R. Nurahmat Damara, “Analisis Pemetaan Ilmiah tentang Perkembangan Explainable Artificial Intelelligence,” EXPLORE, vol. 15, no. 2, pp. 2087–894, Jul. 2025, doi: 10.35200/ex.v15i2.166.

[2] S. Sukma, Memahami Autisme. DIVA Press, 2023.

[3] A. L. Puspanagara, “Penerapan Explainable AI untuk Prediksi Performa Akademik Mahasiswa Menggunakan Random Forest dan SHAP,” Infoman’s, vol. 19, no. 1, pp. 1–7, May 2025, doi: 10.13140/RG.2.2.27853.14565.

[4] M. H. Musyaffa, T. H. Saragih, D. T. Nugrahadi, D. Kartini, and A. Farmadi, “Effectiveness of SMOTE in Enhancing Adult Autism Spectrum Disorder Diagnosis Predictive Performance With Missforest Imputation And Random Forest,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 2, pp. 270–280, Apr. 2025, doi: 10.35882/ijeeemi.v7i2.66.

[5] C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 3rd ed. Lulu.com, 2022. Accessed: Jul. 07, 2025. [Online].Available: https://christophm.github.io/ interpretable-ml-book/

[6] Shinta Delfianti, Khalida Ayuni, Alifah Rizki, and Hijriati Hijriati, “Analisis Karakteristik Anak Berkebutuhan Khusus: Autisme Di Flexi School Banda Aceh,” Ta’rim: Jurnal Pendidikan dan Anak Usia Dini, vol. 5, no. 2, pp. 97–106, May 2024, doi: 10.59059/tarim.v5i2.1244.

[7] Alex J. Smola & S.V.N. Vishwanathan, Introduction to Machine Learning. Cambridge University Press, 2022.

[8] A. Blum, J. Hopcroft, and R. Kannan, Foundations of Data Science. Cambridge University, 2022.

[9] A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems, 3rd ed. O’Reilly Media, Inc., 2022.

[10] A. Novianto and M. D. Anasanti, “Autism Spectrum Disorder (ASD) Identification Using Feature-Based Machine Learning Classification Model,” IJCCS (Indonesian Journal of Computing and Cybernetics.