Identifikasi Autism Spectrum Disorder Menggunakan Model Klasifikasi Machine Learning Berbasis Seleksi Fitur

• ANTON NOVIANTO
• 14210243

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ABSTRAK

ABSTRAK

Nama               : Anton Novianto

NIM                  : 14210243

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang            : Strata Dua (S2)

Konsentrasi      : Data Mining

Judul      : Identifikasi Autism Spectrum Disorder Menggunakan Model Klasifikasi Machine Learning Berbasis Seleksi Fitur

Autism Spectrum Disorder (ASD) adalah salah satu gangguan neurologis perkembangan yang sering kali mempengaruhi kemampuan sosial, komunikasi, dan perilaku individu. Gangguan ini memiliki spektrum gejala yang bervariasi, mulai dari gejala ringan hingga yang lebih parah. Oleh karena itu, diagnosis dini dan akurat ASD sangat penting untuk memberikan intervensi yang sesuai dan membantu individu yang terkena ASD serta keluarga mereka. Pendekatan model klasifikasi machine learning berbasis seleksi fitur dapat menjadi solusi untuk mengatasi tantangan tersebut. Model ini dapat digunakan untuk memprediksi ASD dengan memanfaatkan fitur-fitur yang terkait. Klasifikasi SVM dengan F-Score dan spFSR memiliki hasil kinerja yang sama baiknya dengan hanya menggunakan sepuluh fitur dibandingkan dengan menggunakan seluruh fitur yang tersedia. Kinerja Kaggle memberikan hasil rata – rata kinerja akurasi 96% untuk F-Score dan 98% untuk spFSR, sedangakan UCI memberikan hasil rata – rata kinerja akurasi 97% untuk spFSR, artinya dataset Kaggle memiliki fitur yang lebih robust untuk kinerja spFSR. Penelitian yang dilakukan dengan hanya menggabungkan setengah fitur menunjukkan efisiensi yang dicapai dari metode penelitian tersebut.

KATA KUNCI

ASD,machine learning,Fitur Seleksi,SVM,F-Score

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