Analisa Komparasi dengan Algoritma K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) untuk Prediksi Penyakit Jantung
Abstract
Tingkat keberhasilan deteksi penyakit jantung sangat bergantung pada akurasi model klasifikasi yang digunakan. Penelitian ini bertujuan membandingkan kinerja dua algoritma klasifikasi, yaitu K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM), dalam mendeteksi penyakit jantung menggunakan dataset berjumlah 1025 sampel dengan dua kelas target, yakni sehat dan penyakit jantung. Proses pra-pemrosesan data meliputi pembersihan dan normalisasi fitur medis seperti usia, tekanan darah, serta kadar kolesterol. Evaluasi performa model dilakukan menggunakan metode Confusion Matrix, K-Fold Cross Validation, kurva Receiver Operating Characteristic (ROC), dan kurva Precision-Recall untuk mengukur akurasi, presisi, recall, serta keseimbangan antara presisi dan recall. Hasil pengujian menunjukkan bahwa algoritma KNN unggul dalam menghasilkan akurasi tinggi yaitu 99% dengan AUC ROC sempurna 1.00 dan presisi yang hampir konsisten sepanjang recall, sementara SVM menunjukkan performa stabil dengan akurasi 91%, AUC ROC 0.97, dan AP Precision-Recall sebesar 0.96. Penelitian ini menegaskan efektivitas KNN dalam menghasilkan prediksi penyakit jantung yang sangat akurat dengan potensi risiko overfitting pada parameter k kecil, sedangkan SVM memberikan kestabilan model dengan kemampuan generalisasi yang lebih baik. Temuan ini diharapkan dapat menjadi referensi dalam pemilihan algoritma klasifikasi yang sesuai untuk mendukung diagnosis penyakit jantung secara klinis.
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