Komparasi Akurasi Algoritme CART Dan Neural Network Untuk Diagnosis Penyakit Diabetes Retinopathy
DOI:
https://doi.org/10.31154/cogito.v7i1.304.121-134Abstract
Penyakit Diabetes Retinopathy atau DR adalah salah satu komplikasi mikrovaskular diabetes melitus dengan angka prevalensi yang cukup tinggi yang bisa menyebabkan kematian. Penderita DR hingga saat ini masih sulit disembuhkan karena mayoritas penderita melakukan pemeriksaan di saat kondisi penyakit telah memasuki tahap berbahaya, hal ini dikarenakan sifat dari penyakit DR ini tidak menunjukkan gejala yang terlihat bila masih pada tahap awal. Penelitian ini menguji diagnosis penyakit diabetes retinopathy dengan melakukan klasiifikasi menggunakan metode data mining. Metode yang digunakan ialah algoritme Classification And Regression Trees (CART) dan Algoritme Neural Network menggunakan dataset diambil dari UCI Repository Learning diperoleh daro Universitas Debreen, Hongaria. Adapun metode validasi dan evaluasi yang digunakan dalam penelitian ini yaitu 10-cross validation dan confusion matrix. Hasil dari akurasi pada algoritme CART yaitu 63.4231% dengan nilai precision 0.64%, Recall 0.634%, dan F-Measure 0.634% dan algoritme Neural Network mendapatkankan nilai akurasi sebesar 72.285% dengan nilai precision 0.723%, Recall 0.723%, dan F-Measure 0.723%. Dari hasil tersebut dapat disimpulkan bahwa algoritme Neural Network lebih baik dalam mendiagnosis penyakit diabetes retinopathy. Kata kunci— Klasifikasi, Diagnosis, Diabetes Retinopathy, Algoritme, CART, Neural NetworkReferences
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