PREDIKSI STATUS ANEMIA DENGAN PENDEKATAN PEMBELAJARAN MESIN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN SELEKSI FITUR FIREFLY ALGORITHM
Keywords:
Akurasi, Anemia, Firefly Algorithm, Seleksi Fitur, SVMAbstract
Anemia merupakan kelainan tubuh yang ditandai dengan rendahnya kadar hemoglobin (Hb) dalam sel darah, dan dapat menjadi masalah kesehatan yang serius terlebih pada remaja perempuan jika tidak segera diobati dengan baik. Penelitian ini dilakukan untuk memprediksi status anemia berdasarkan data pasien menggunakan algoritma SVM dengan pemilihan fitur Firefly Algorithm untuk meningkatkan akurasi. Pengujian dilakukan dengan menggunakan empat kernel algoritma SVM yaitu kernel Linear, Polynomial, RBF dan Sigmoid. Hasil penelitian menunjukkan bahwa penggabungan FA dan SVM dapat meningkatkan akurasi pada tiga kernel SVM yaitu kernel linear dari akurasi awal 98.95% menjadi 99.65%, kernel polynomial akurasi awal 94.74% menjadi 98.60%, pada kernel RBF akurasi awal 93.68% menjadi 98.25%, namun pada kernel sigmoid akurasi mengalami penurunan dari akurasi awal 47.02% menjadi 12.98%. Kesimpulannya, penerapan FA untuk memilih fitur-fitur penting pada SVM efektif dan berdampak pada peningkatan akurasi untuk tiga kernel SVM dan penurunan akurasi pada satu kernel hal tersebut terjadi karena underfitting. Seleksi fitur menjadi efektif jika menghasilkan kombinasi fitur yang tepat dan dapat menjadi tidak efektif jika menghasilkan kombinasi fitur yang tidak tepat.
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