Reduksi Fitur untuk Optimasi Prediksi Penyakit Ginjal Kronis menggunakan Radial Basis Function Neural Network
DOI:
https://doi.org/10.32493/informatika.v7i2.12550Keywords:
reduksi fitur, Principal Component Analysis, Linear Discriminant Analysis, penyakit ginjal kronisAbstract
Penyakit ginjal kronis menjadi salah satu penyebab kematian di dunia. Penderita penyakit ginjal kronis memerlukan perawatan yang cukup serius. Kenyataannya gejala dari penyakit ginjal kronis tidak dapat diketahui secara langsung, melainkan secara bertahap hingga kondisi ginjal pasien sudah tidak berfungsi dengan baik dan sulit disembuhkan. Karena itu menjadi penting untuk dapat mendeteksi sedini mungkin kemungkinan pasien menderita penyakit ginjal kronis. Prediksi penyakit ginjal kronis sudah cukup banyak dilakukan dengan berbagai metode data mining dan machine learning. Penelitian ini membandingan tiga metode reduksi fitur, yaitu Principal Component Analysis, Liniear Discriminant Component, serta gabungan Principal Component Analysis dan Linear Discriminant Analysis untuk mengoptimalkan prediksi penyakit ginjal kronis. Pada tahap pelatihan dan pengujian digunakan metode klasifikasi Radial Basis Function Neural Network. Hasil uji menunjukkan bahwa metode reduksi fitur gabungan Principal Analysis Component dan Linear Discriminant Analysis menunjukkan performa terbaik dengan nilai akurasi, recall, dan precision secara berturut-turut sebesar 93,5%, 91,1% dan 97,7%.
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