Klasifikasi Gangguan Autisme Berdasarkan Dataset Perilaku Anak Menggunakan Metode K-Nearest Neighbor (KNN)
Keywords:
Gangguan Spektrum Autisme, klasifikasi perilaku, deteksi dini, K-Nearest Neighbor, machine learningAbstract
Deteksi dini Gangguan Spektrum Autisme (Autism Spectrum Disorder/ASD) sangat penting untuk mendukung intervensi perkembangan pada anak; namun, proses identifikasi awal masih menghadapi berbagai tantangan karena bergantung pada observasi manual yang memerlukan tenaga ahli terlatih dan dapat menyebabkan keterlambatan dalam diagnosis. Penelitian ini bertujuan untuk mengembangkan model klasifikasi berbasis data perilaku anak yang mampu memprediksi kemungkinan ASD secara objektif sebagai dukungan untuk skrining dini. Metode yang digunakan adalah pendekatan machine learning dengan algoritma K-Nearest Neighbor (KNN). Data penelitian diperoleh dari Toddler Autism Dataset Juli 2018 yang berisi 1.054 sampel dengan 19 variabel. Tahapan penelitian meliputi identifikasi variabel, prapemrosesan data, konversi variabel kategorikal ke bentuk numerik, normalisasi fitur, pembagian data menjadi 80% data latih dan 20% data uji, pelatihan model dengan nilai k = 5, serta evaluasi menggunakan akurasi dan confusion matrix. Hasil penelitian menunjukkan bahwa model KNN mencapai akurasi sebesar 97,16%, dengan nilai presisi dan recall yang tinggi pada kedua kelas, sehingga mampu membedakan secara efektif antara anak yang memiliki dan tidak memiliki indikasi ASD. Berdasarkan kinerja tersebut, dapat disimpulkan bahwa algoritma KNN memiliki potensi yang kuat sebagai pendekatan komputasi yang tepat untuk mendukung skrining dini ASD berbasis data perilaku, khususnya dalam lingkungan pendidikan atau klinis yang memerlukan dukungan identifikasi yang cepat, terukur, dan terstandar.References
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