KOMBINASI METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES UNTUK KLASIFIKASI DATA
Abstract
Data mining banyak digunakan untuk membantu menentukan keputusan dengan memprediksi tren data masa depan. K-Nearest Neighbor dan Naïve Bayes merupakan metode-metode data mining untuk klasifikasi data yang cukup populer. Kedua metode tersebut masing-masing memiliki kelemahan. Proses pengolahan data dengan metode KNN lebih lama dibanding dengan Naïve Bayes. Berdasarkan penelitian yang dilakukan oleh beberapa peneliti, metode KNN dan Naïve Bayes memiliki nilai keakuratan yang cukup tinggi Pada penelitian ini, Naïve Bayes menghasilkan nilai keakuratan yang lebih kecil dibanding metode KNN. Dari permasalahan di atas, maka diusulkan metode kombinasi KNN dan Naïve Bayes untuk mengatasi kelemahan tersebut. Metode KNN, Naïve Bayes, dan metode kombinasi KNNNaïve Bayes diujikan pada data yang sama untuk memperoleh hasil perbandingan persentase keakuratan dan lama waktu proses. Hasil pengujian ketiga metode dengan Nursery dataset, membuktikan bahwa, metode kombinasi KNN-Naïve Bayes berhasil mengatasi kelemahan pada Naïve Bayes ataupun KNN. Proses pengolahan data metode kombinasi KNN-Naïve Bayes lebih cepat dibanding KNN dan Naïve Bayes. Selain itu hasil persentase keakuratan yang diperoleh metode KNN-Naïve Bayes lebih tinggi dibanding dengan metode KNN dan Naïve Bayes.
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