Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jambura Journal of Electrical and Electronics Engineering
Septiana Septiana
Universitas Amikom Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Classification of Evidence-Based Practices Learning Strategies in Autism Spectrum Disorder Using Random Forest Septiana Septiana; Dwi Krisbiantoro; Suliswaningsih Suliswaningsih
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39495

Abstract

Selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder requires careful consideration of children’s diverse ability profiles. This study developed a classification model for Evidence-Based Practices learning strategies using the Random Forest algorithm based on a primary dataset consisting of 106 records. The features used include child age, gender, verbal ability, Autism Spectrum Disorder severity level, learning media, and language learning difficulties. The classification target consists of four classes: ABA, Visual Method, PECS, and Speech Therapy. Model evaluation was conducted using an 80:20 hold-out split, Stratified 5-Fold Cross Validation, Repeated Stratified Cross Validation, ablation test, and feature importance analysis. In the 80:20 hold-out scenario, Random Forest achieved an accuracy of 45.45% and an F1-Macro score of 0.4393. In Stratified 5-Fold Cross Validation, the model obtained an average accuracy of 38.61% ± 7.17% and an F1-Macro score of 0.3803 ± 0.0736. The repeated cross-validation results showed an average accuracy of 34.45% ± 9.46% and an F1-Macro score of 0.3332 ± 0.0967. These findings indicate that Random Forest is able to form an initial classification model; however, its performance remains low to moderate. The limitations of this study lie in the limited dataset size, the relatively small number of samples per class, and the absence of external validation; therefore, the model cannot yet be used as a final recommendation system. This study is positioned as a preliminary study to support the development of a Decision Support System for selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder.
Co-Authors Dwi Krisbiantoro Suliswaningsih