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Artificial Intelligence-Based Leveling System for Determining Severity Level of Autism Spectrum Disorder

Scientific Journal of Informatics · Vol. 12 No. 4 (2025) · hlm. 567–576

DOI
10.15294/sji.v12i4.14440
ISSN
2407-7658, 2460-0040
Terbit
2025-11-19
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Purpose: The aim of this research is to analyze the use of an artificial intelligence (AI)-based leveling system to determine the severity of autism spectrum disorders (ASD). Methods: The research method is a systematic literature review. This study addresses three key questions: (i) What factors are used to determine ASD severity? (ii) What algorithms or AI models are used in classifying ASD severity? (iii) What are the results of this AI-based leveling system in terms of severity levels or categories? Results: The study results identified several key factors that influence ASD severity, including age, IQ, genetic and neurological factors, co-occurring mental health conditions, and sociodemographic variables. Various AI algorithms, including machine learning and deep learning techniques, are used to classify the severity of ASD. The results of this study highlight the effectiveness of AI in providing objective, consistent, and measurable assessments of ASD severity, although challenges such as data quality and ethical considerations remain. AI-based leveling systems show significant potential in improving assessment and intervention processes for ASD. Novelty: This research systematically synthesizes studies on AI-driven ASD severity assessment, providing insights into crucial variables for AI-based evaluation tools. By analyzing the factors influencing severity and the effectiveness of AI models, this study identifies promising approaches for classification. The findings offer valuable contributions to the development of AI-based tools in clinical and educational applications. Further research is necessary to improve AI reliability, address biases, and maximize its potential in ASD assessment and intervention.

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  • State University of Gorontalo
  • Indonesia University of Education
  • Sunan Gunung Djati State Islamic University Bandung
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Rekan penulisOrganisasiArtikel bersama
Nova Elisa NtobuoState University of Gorontalo1
Asri ArbieState University of Gorontalo1
Sitti SuhadaState University of Gorontalo1
Lillyan HadjaratieState University of Gorontalo1
Hasan S. PanigoroState University of Gorontalo1
Femy Mahmud SahamiState University of Gorontalo1
Harto S. MalikState University of Gorontalo1
WardonoAfiliasi tidak diketahui1
T. WidodoAfiliasi tidak diketahui1
Nurhamzah NurhamzahSunan Gunung Djati State Islamic University Bandung1
Hariman Surya SiregarSunan Gunung Djati State Islamic University Bandung1
Miftahul FikriSunan Gunung Djati State Islamic University Bandung1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Artificial Intelligence-Based Leveling System for Determining Severity Level of Autism Spectrum Disorder</dc:title>
	<dc:creator xml:lang="en">Rasim, R</dc:creator>
	<dc:creator xml:lang="en">Munir, M</dc:creator>
	<dc:creator xml:lang="en">Wihardi, Yaya</dc:creator>
	<dc:creator xml:lang="en">Ningrayati Amali, Lanto </dc:creator>
	<dc:subject xml:lang="en">Autism spectrum disorder</dc:subject>
	<dc:subject xml:lang="en">Severity level</dc:subject>
	<dc:subject xml:lang="en">Leveling</dc:subject>
	<dc:subject xml:lang="en">Artificial intelligence</dc:subject>
	<dc:subject xml:lang="en">Machine learning</dc:subject>
	<dc:description xml:lang="en">Purpose: The aim of this research is to analyze the use of an artificial intelligence (AI)-based leveling system to determine the severity of autism spectrum disorders (ASD).
Methods: The research method is a systematic literature review. This study addresses three key questions: (i) What factors are used to determine ASD severity? (ii) What algorithms or AI models are used in classifying ASD severity? (iii) What are the results of this AI-based leveling system in terms of severity levels or categories? 
Results: The study results identified several key factors that influence ASD severity, including age, IQ, genetic and neurological factors, co-occurring mental health conditions, and sociodemographic variables. Various AI algorithms, including machine learning and deep learning techniques, are used to classify the severity of ASD. The results of this study highlight the effectiveness of AI in providing objective, consistent, and measurable assessments of ASD severity, although challenges such as data quality and ethical considerations remain. AI-based leveling systems show significant potential in improving assessment and intervention processes for ASD.
Novelty: This research systematically synthesizes studies on AI-driven ASD severity assessment, providing insights into crucial variables for AI-based evaluation tools. By analyzing the factors influencing severity and the effectiveness of AI models, this study identifies promising approaches for classification. The findings offer valuable contributions to the development of AI-based tools in clinical and educational applications. Further research is necessary to improve AI reliability, address biases, and maximize its potential in ASD assessment and intervention.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-11-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/14440</dc:identifier>
	<dc:identifier>10.15294/sji.v12i4.14440</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 12 No. 4: Special Issue; 567-576</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v12i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/14440/7398</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>