Prediksi Penyakit Jantung Menggunakan Attribute Weighting k-Nearest Neighbor
DOI:
https://doi.org/10.22441/incomtech.v13i2.17883Kata Kunci:
Prediksi Penyakit Jantung, k-Nearest Neighbor, Pembobotan Atribut, Weighted Euclidean DistanceAbstrak
Penyakit kardiovaskular atau lebih dikenal dengan penyakit jantung menjadi salah satu penyebab kematian tertinggi di Indonesia dan di tingkat global. Selain pola hidup sehat untuk mencegah penyakit tersebut, deteksi dini terhadap resiko penyakit jantung dapat dilakukan dengan data mining atau machine learning salah satunya k-NN. k-NN adalah salah satu metode data mining paling sederhana dan kuat dalam konsistensi hasil klasifikasi, akan tetapi memiliki kekurangan yaitu memberikan bobot yang sama kepada semua atribut. Penelitian ini mengusulkan pembobotan pada atribut untuk mengatasi kelemahan tersebut. Prediksi penyakit jantung digunakan untuk menggambarkan kinerja metode usulan. Pada penelitian ini menggunakan dataset Heart Disease, sebuah dataset publik dari University of California Irvine. Dengan menggunakan nilai k 3, 5, 7, 9 diperoleh rata-rata kinerja metode usulan sebesar 79,87% lebih baik dibandingkan Chi-Square k-NN 79,08% dan k-NN klasik 65,89%. Penelitian ini menyimpulkan bahwa metode pembobotan atribut berhasil mengatasi kekurangan k-NN, jadi metode usulan cocok untuk prediksi penyakit jantung.
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