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May 15, 2025
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July 3, 2025
Published
July 6, 2025
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Abstract

Gangguan spektrum autisme adalah kondisi neurologis yang ditandai dengan gangguan kemampuan komunikasi, keterasingan sosial, dan perilaku repetitif pada individu. Organisasi kesehatan global menghadapi kesulitan dalam membangun sistem diagnostik ASD yang efektif yang memfasilitasi analisis yang tepat dan prediksi autisme dini. Penelitian ini menyajikan pendekatan untuk prediksi dini anak-anak dengan ASD dengan memanfaatkan variabel signifikan melalui metode pembelajaran mesin. Data set terdiri dari kasus ASD sebanyak 1250 data dimana diambil 5 variabel yang sangat efektif untuk menghiutng koefisien korelasi pearson yaitu: jenis kelamin, keterlambatan bisaca, penyakit kuning, gangguan genetik dan riwatat keluarga. Analisis dataset mengunakan lima teknik Machine Learning yaitu: Naïve Bayes, K-Nearest Neighbor, Decision Tree, Support Vector Machine dan AdaBoostM1. Pengukuran akurasi, presisi, waktu prediksi, recall dan F1-score digunakan untuk menguji algoritma ML yang digunakan. Hasil pengujian menunjukkan bahwa metode Naïve Bayes dan K-Nearest Neighbor dengan tingkat akurasi sebesar 99,2% dan 96,3% dan waktu prediksi yang minimal sebesar 0,31 dan 0,46 detik. Sedangkan metode yang menunjukkan penurusan akurasi adalah Decision Tree dan AdaBoostM1 dengan masing-masing sebesar 95,8% dan 88,6%. Sedangkan kinerja paling rendah adalah metode Support Vector Machine dengan tingkat akurasi sebesar 81,2% dan waktu prediksi tertinggi sebesar 0,82 detik.

Keywords

gangguan spektrum autis rekayasa kesehatan pembelajaran mesin prediksi

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Copyright (c) 2025 Taryadi, Era Yunianto, Mosses Aidjili

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Taryadi, Yunianto, E., & Aidjili, M. (2025). Diagnosa Awal Autisme pada Anak dengan Algoritma Machine Learning. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 4(2), 182–189. https://doi.org/10.20885/snati.v4.i2.40317
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