Model Arsitektur Sistem Informasi Terintegrasi AI untuk Pemantauan dan Intervensi Anak dengan Autism Spectrum Disorder
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Jul 7, 2026
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Meningkatnya jumlah anak dengan Autism Spectrum Disorder (ASD) di Indonesia menunjukkan tren peningkatan yang berkelanjutan; namun, masih dihadapkan dengan berbagai tantangan mendasar, seperti rendahnya pemahaman orang tua mengenai deteksi dini, terbatasnya ketersediaan tenaga medis dan terapis, serta tidak adanya sistem informasi dan pusat data yang terintegrasi. Kondisi ini mengakibatkan data diagnosis, terapi, dan perkembangan anak dengan ASD tersebar dan tidak terdokumentasi secara berkelanjutan. Di sisi lain, kemajuan teknologi informasi, khususnya Artificial Intelligence (AI), menawarkan potensi signifikan dalam mendukung deteksi dini, analisis perilaku, dan penyediaan rekomendasi intervensi yang personal dan berbasis data. Studi ini bertujuan untuk merancang model arsitektur sistem informasi abstrak yang terintegrasi dengan AI untuk mendukung proses pemantauan dan intervensi bagi anak dengan ASD. Metode yang digunakan adalah pendekatan Design Science Research (DSR), yang meliputi tahapan identifikasi masalah, desain model, pengembangan artefak konseptual, dan validasi melalui skenario kasus penggunaan. Hasil studi berupa model arsitektur sistem informasi terintegrasi yang terdiri dari aplikasi pengguna, lapisan integrasi data, modul analitik AI, dan mekanisme human-in-the-loop. Kontribusi studi ini adalah kerangka sistem informasi konseptual terintegrasi berbasis AI yang dirancang untuk mengatasi keterbatasan sistem yang ada, yang umumnya terfragmentasi dan terisolasi, serta berfungsi sebagai dasar untuk pengembangan sistem pemantauan dan intervensi ASD di Indonesia.
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Zamasi, H. C., & Setiawan, A. S. (2026). Model Arsitektur Sistem Informasi Terintegrasi AI untuk Pemantauan dan Intervensi Anak dengan Autism Spectrum Disorder. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 7(2). https://doi.org/10.62527/jitsi.7.2.616
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Referensi
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[28] D. Ahuja, A. Sarkar, S. Chandra, and P. Kumar, “Wearable technology for monitoring behavioral and physiological responses in children with autism spectrum disorder: A literature review,” Technol. Disabil., vol. 34, no. 2, pp. 69–84, May 2022, doi: 10.3233/TAD-210349.
[29] A. Robaczewski, J. Bouchard, K. Bouchard, and S. Gaboury, “Socially Assistive Robots: The Specific Case of the NAO,” Int. J. Soc. Robot., vol. 13, no. 4, pp. 795–831, Jul. 2021, doi: 10.1007/s12369-020-00664-7.
[30] J. Guggemos, S. Seufert, and S. Sonderegger, “Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT,” British Journal of Educational Technology, vol. 51, no. 5, pp. 1864–1883, Sep. 2020, doi: 10.1111/bjet.13006.
[31] K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems, vol. 24, no. 3, pp. 45–77, Dec. 2007, doi: 10.2753/MIS0742-1222240302.
[2] S. Zhang, “AI-assisted early screening, diagnosis, and intervention for autism in young children,” Front. Psychiatry, vol. 16, Apr. 2025, doi: 10.3389/fpsyt.2025.1513809.
[3] V. R. I and Z. Hussain, “Early Detection of Developmental Delays: The Role of Artificial Intelligence in Transforming Pediatric Care,” International Journal For Multidisciplinary Research, vol. 7, no. 2, Mar. 2025, doi: 10.36948/ijfmr.2025.v07i02.39950.
[4] A. Abdul-kareem, Z. Fayed, S. Rady, S. Amin, and B. Nema, “Advances in Decision Support Systems’ design aspects: architecture, applications, and methods,” International Journal of Intelligent Computing and Information Sciences, vol. 23, no. 2, pp. 74–104, Jun. 2023, doi: 10.21608/ijicis.2023.160460.1216.
[5] Julia Maria Van Tiel, Anakku Gifted Terlambat Bicara. 2016.
[6] R. S. Dhariyal, V. Kimothi, and S. Singh, “A Review on Autism,” Research Journal of Pharmacology and Pharmacodynamics, vol. 11, no. 2, p. 76, 2019, doi: 10.5958/2321-5836.2019.00013.2.
[7] R. M. Botelho, A. L. M. Silva, and A. U. Borbely, “The Autism Spectrum Disorder and Its Possible Origins in Pregnancy,” Int. J. Environ. Res. Public Health, vol. 21, no. 3, p. 244, Feb. 2024, doi: 10.3390/ijerph21030244.
[8] R. A. J. de Belen, T. Bednarz, A. Sowmya, and D. Del Favero, “Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019,” Transl. Psychiatry, vol. 10, no. 1, p. 333, Sep. 2020, doi: 10.1038/s41398-020-01015-w.
[9] D. U. Reddy, K. V. P. Kumar, B. Ramakrishna, and U. G. Sankar, “Development of computer vision based assistive software for accurate analysis of autistic child stereotypic behavior,” in 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), IEEE, Apr. 2023, pp. 1–5. doi: 10.1109/RAEEUCCI57140.2023.10134091.
[10] K. Xu, B. Ji, Z. Wang, J. Liu, and H. Liu, “An Auxiliary Screening System for Autism Spectrum Disorder Based on Emotion and Attention Analysis,” in 2020 IEEE International Conference on SMC, IEEE, Oct. 2020, pp. 2299–2304. doi: 10.1109/SMC42975.2020.9283365.
[11] L. A. LeBlanc, K. B. Geiger, R. A. Sautter, and T. M. Sidener, “Using the Natural Language Paradigm (NLP) to increase vocalizations of older adults with cognitive impairments,” Res. Dev. Disabil., vol. 28, no. 4, pp. 437–444, Jul. 2007, doi: 10.1016/j.ridd.2006.06.004.
[12] H. MacFarlane, A. C. Salem, L. Chen, M. Asgari, and E. Fombonne, “Combining voice and language features improves automated autism detection,” Autism Research, vol. 15, no. 7, pp. 1288–1300, Jul. 2022, doi: 10.1002/aur.2733.
[13] N. M. Alzrayer, R. Aldabas, A. Alhossein, and H. Alharthi, “Naturalistic teaching approach to develop spontaneous vocalizations and augmented communication in children with autism spectrum disorder,” Augmentative and Alternative Communication, vol. 37, no. 1, pp. 14–24, Jan. 2021, doi: 10.1080/07434618.2021.1881825.
[14] K. E. Laski, M. H. Charlop, and L. Schreibman, “Training Parents to Use The Natural Language Paradigm to Increase Their Autistic Children’s Speech,” J. Appl. Behav. Anal., vol. 21, no. 4, pp. 391–400, Dec. 1988, doi: 10.1901/jaba.1988.21-391.
[15] S. Pandya, S. Jain, and J. P. Verma, “AI based Classification for Autism Spectrum Disorder Detection using Video Analysis,” in 2022 IEEE Bombay Section Signature Conference (IBSSC), IEEE, Dec. 2022, pp. 1–6. doi: 10.1109/IBSSC56953.2022.10037438.
[16] M. T. Ali et al., “A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework,” Sci. Rep., vol. 13, no. 1, p. 17048, Oct. 2023, doi: 10.1038/s41598-023-43478-z.
[17] S. Fiza, S. MP, and G. Shukla, “Predictive Analytics and AI for Early Diagnosis and Intervention in Autism Spectrum Disorders,” IEEE, Dec. 2023, pp. 1–8. doi: 10.1109/ICTBIG59752.2023.10456267.
[18] N. Xing, “Artificial Intelligence to support Children with Autism,” Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), vol. 2, no. 1, pp. 31–43, Oct. 2024, doi: 10.60087/vol2iisue1.p43.
[19] J. SHI, “The Application of AI as Reinforcement in the Intervention for Children With Autism Spectrum Disorders (ASD),” J. Educ. Develop. Psychol., vol. 9, no. 2, p. 17, Jun. 2019, doi: 10.5539/jedp.v9n2p17.
[20] P. Anagnostopoulou, V. Alexandropoulou, G. Lorentzou, A. Lykothanasi, P. Ntaountaki, and A. Drigas, “Artificial Intelligence in Autism Assessment,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 06, p. 95, Mar. 2020, doi: 10.3991/ijet.v15i06.11231.
[21] N. Wankhede et al., “Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects,” Asian J. Psychiatr., vol. 101, p. 104241, Nov. 2024, doi: 10.1016/j.ajp.2024.104241.
[22] R. Pandey, N. Maurya, P. Maurya, and P. Saxena, “Predictive approach for Autism Detection using Computer Vision and Deep Learning,” in 2024 MITADTSoCiCon, IEEE, Apr. 2024, pp. 1–6. doi: 10.1109/MITADTSoCiCon60330.2024.10575142.
[23] P. Mittal et al., “Effect of immersive virtual reality-based training on cognitive, social, and emotional skills in children and adolescents with autism spectrum disorder: A meta-analysis of randomized controlled trials,” Res. Dev. Disabil., vol. 151, p. 104771, Aug. 2024, doi: 10.1016/j.ridd.2024.104771.
[24] H. H. S. Ip et al., “Enhance emotional and social adaptation skills for children with autism spectrum disorder: A virtual reality enabled approach,” Comput. Educ., vol. 117, pp. 1–15, Feb. 2018, doi: 10.1016/j.compedu.2017.09.010.
[25] N. Didehbani, T. Allen, M. Kandalaft, D. Krawczyk, and S. Chapman, “Virtual Reality Social Cognition Training for children with high functioning autism,” Comput. Human Behav., vol. 62, pp. 703–711, Sep. 2016, doi: 10.1016/j.chb.2016.04.033.
[26] L. Billeci et al., “An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies,” Front. Neurosci., vol. 10, Jun. 2016, doi: 10.3389/fnins.2016.00276.
[27] R. Rahman, M. Samiun, N. Mohammad, M. Prabha, and A. M. Zaman, “Wearable Technology for Real-Time Monitoring of Stress and Behavior in Autistic Individuals in the USA,” Journal of Management World, vol. 2025, no. 2, pp. 70–77, Jan. 2025, doi: 10.53935/jomw.v2024i4.869.
[28] D. Ahuja, A. Sarkar, S. Chandra, and P. Kumar, “Wearable technology for monitoring behavioral and physiological responses in children with autism spectrum disorder: A literature review,” Technol. Disabil., vol. 34, no. 2, pp. 69–84, May 2022, doi: 10.3233/TAD-210349.
[29] A. Robaczewski, J. Bouchard, K. Bouchard, and S. Gaboury, “Socially Assistive Robots: The Specific Case of the NAO,” Int. J. Soc. Robot., vol. 13, no. 4, pp. 795–831, Jul. 2021, doi: 10.1007/s12369-020-00664-7.
[30] J. Guggemos, S. Seufert, and S. Sonderegger, “Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT,” British Journal of Educational Technology, vol. 51, no. 5, pp. 1864–1883, Sep. 2020, doi: 10.1111/bjet.13006.
[31] K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems, vol. 24, no. 3, pp. 45–77, Dec. 2007, doi: 10.2753/MIS0742-1222240302.