PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN SUPPORT VECTOR MACHINE DAN PYTHON PADA BASIS DATA PASIEN DI CLEVELAND

Dwi Sidik Permana; Astried Silvanie

Dwi Sidik Permana Institut Bisnis dan Informatika (IBI) Kosgoro 1957
Astried Silvanie Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Keywords: Prediksi Penyakit Jantung, Machine Learning, Support Vector Machine, SVM, Python Machine Learning

Abstract

Support Vector Machine (SVM) digunakan dalam penelitian ini untuk memprediksi penyakit jantung berdasarkan 13 kondisi medis pasien. Kondisi medis ini digunakan sebagai atribut predikator dalam penelitian ini. Keluaran yang ingin diprediksi berupa kelas target bernilai 1 jika pasien penyakit jantung dan 0 jika pasien bukan penyakit jantung. Pelatihan dilakukan dengan Python dan pustaka scikit. SVM diuji menggunakan empat macam kernel yaitu linear, RBF, polynomial dan sigmod. Dari hasil pelatihan model dengan nilai metrik terbaik didapatkan jika menggunakan kernel linear. Nilai metrik akurasi sama dengan 90.11%, presisi 90.38% dan recall 92.15% dengan kernel linear

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