PREDIKSI PENYAKIT JANTUNG DENGAN MENGGUNAKAN METODE ALGORITMA C4.5 BERBASIS BACKWARD ELIMINATION
Abstract
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Penyakit jantung merupakan penyebab utama kematian di dunia selama 10 tahun terakhir, pada tahun 2003 World Health Organization (WHO) melaporkan bahwa 29,2% dari total kematian global akibat Cardio Vascular Disease (CVD), yang menjadi penyebab utama atas kematian di negara berkembang karena perubahan gaya hidup, budaya kerja dan kebiasaan makanan. Algoritma C4.5 merupakan pengklasifikasian yang paling sederhana, mudah diimplementasikan. Namun Algoritma C4.5 masih memiliki kelemahan dalam menangani data dalam dimensi tinggi. Penelitian ini bertujuan untuk menerapkan algoritma backward elimination dengan seleksi atribut split validation sehingga dapat meningkatkan prediksi serangan penyakit jantung. Dari penelitian ini menguji 4 dataset yang ada di UCI Machine Learning yang awalnya menggunakan algoritma C4.5 dalam pengklasifikasian atribut dengan hasil pencapaian yaitu hungarian dataset 82.95%, Cleveland dataset 56.29 %, Switzerland dataset 50.00% dan VA dataset 35.00%. Setelah diuji dengan algoritma backward elimination maka prediksi meningkat pada setiap dataset, hasil pencapaian yaitu hungarian dataset 93.10 %, Cleveland dataset 67.21 %, Switzerland dataset 75.00% dan VA dataset 40.00% dalam penyeleksian atribut pada serangan penyakit jantung.
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DOI: https://doi.org/10.33857/patj.v8i2.913
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