Performansi Seleksi Fitur pada Metode Multi Klasifikasi untuk Deteksi Dini Autisme Berbasis Citra Wajah Anak
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Published:
May 31, 2024
Keywords:
Autisme, Anak, Deteksi, Machine learning, Multi-klasifikasi
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Abstract
Deteksi dini terhadap gangguan spektrum autisme (ASD) pada anak sangat penting untuk memberikan intervensi dan terapi tepat waktu. Deteksi dini secara tepat dapat membantu meningkatkan kualitas hidup anak yang terindikasi ASD. Metode pendekatan deteksi dapat dilakukan dengan observasi klinis dan kuesioner psikologi, tetapi metode ini sering kali subjektif dan membutuhkan waktu dalam mengetahui hasilnya. Sehingga dengan melihat permasalahan yang ada, maka penelitian ini bertujuan bertujuan untuk mengembangkan dan mengevaluasi model klasifikasi berbasis multi-klasifikasi dan metode seleksi serta jumlah fitur pada citra wajah untuk mendeteksi secara dini pada ASD. Hasil pengujian menunjukkan perpaduan metode klasifikasi logistik regresi dengan seleksi fitur ANOVA dengan menggunakan 150 fitur menghasilkan performansi terbaik dari sisi akurasi sebesar 0.9688, presisi sebesar 0.9687, dan recall sebesar 0.9688, dibandingkan dengan penggunaan metode seleksi fitur Information Gain. Hasil pengujian menunjukkan metode Logistic Linear Regression memiliki keunggulan dalam melakukan klasifikasi pada kelas biner dengan fitur yang terbatas.
Article Details
How to Cite
Pradipta, G. A., & Putu Desiana Wulaning Ayu. (2024). Performansi Seleksi Fitur pada Metode Multi Klasifikasi untuk Deteksi Dini Autisme Berbasis Citra Wajah Anak. Jurnal Sistem Dan Informatika (JSI), 18(2), 167-176. https://doi.org/10.30864/jsi.v18i2.611
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H. Ravishankar et al., “Understanding the Mechanisms of Deep Transfer Learning for Medical Images,” pp. 68–76, 2017, doi: 10.1007/978-3-319-46976-8.
