Implementasi Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor untuk Klasifikasi Penyakit Ginjal Kronik
Implementation of Naïve Bayes Classifier and K-Nearest Neighbor Algorithms for Chronic Kidney Disease Classification
DOI:
https://doi.org/10.57152/malcom.v4i2.1229Keywords:
Chronic Kidney Disease, Classification, Data Mining, KNN, NBCAbstract
Ginjal adalah salah satu organ vital yang memiliki peranan sangat penting dalam tubuh dan memiliki fungsi untuk menjaga keseimbangan metabolishme tubuh dengan mengeluarkan racun dari dalam tubuh dan limbah metabolisme dalam bentuk urine. Penyakit ginjal kronik ialah kondisi di mana ginjal mengalami penurunan fungsi yang berlangsung dalam jangka waktu yang lama. Jumlah nilai prelevansi penderita PGK di Indonesia yang terbilang besar. Oleh karena itu dilakukan klasifikasi Penyakit ginjal kronik dengan algoritma Naïve Bayes Classifier (NBC) dan K- nearest Neighbor (KNN) yang mempunyai nilai akurasi yang baik. Berdasarkan Hasil penelitian yang diperoleh klasifikasi PGK menggunakan algoritma NBC memiliki akurasi sebesar 94,25%, rata-rata nilai recall 94,23%, presisi 98,40% dan AUC 0,961, Sedangkan klasifikasi menggunakan algoritma KNN memiliki akurasi sebesar 77,79%, recall 95,06%, presisi 80,20% dan AUC sebesar 0,627. Dari kedua hasil menunjukan bahwa klasifikasi menggunakan algoritma NBC lebih baik dibanding menggunakan algoritma KNN.
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