Pengembangan Model Support Vector Machine untuk Meningkatkan Akurasi Klasifikasi Diagnosis Penyakit Jantung
DOI:
https://doi.org/10.32493/jtsi.v7i3.42254Kata Kunci:
Support Vector Machine; Supervised Learning; Klasifikasi; Penyakit JantungAbstrak
Penyakit jantung merupakan salah satu masalah kesehatan serius yang menyebabkan risiko kematian tinggi di seluruh dunia. Faktor-faktor pemicunya meliputi kolesterol tinggi, diabetes, dan tekanan darah tinggi. Oleh karena itu, prediksi dini penyakit jantung adalah langkah awal yang sangat penting untuk mengurangi risiko kematian. Makalah ini mengusulkan model klasifikasi penyakit jantung baru yang berbasis algoritma Support Vector Machine (SVM) untuk meningkatkan performa deteksi penyakit. Untuk meningkatkan akurasi diagnosis, kami menerapkan teknik pemilihan fitur dan grid search. Kinerja model yang ditingkatkan divalidasi dengan membandingkannya dengan model sederhana menggunakan confusion matrix. Model yang ditingkatkan mencapai akurasi 96,56%, menunjukkan peningkatan akurasi sebesar 8,91% dibandingkan model sebelumnya yang hanya mencapai tingkat akurasi sebesar 87,65%. Selain itu, jumlah fitur yang digunakan dikurangi dari 14 menjadi 8, sehingga mengurangi beban komputasi dari 100% menjadi sekitar 32%. Hasil ini menunjukkan bahwa SVM yang ditingkatkan menawarkan kinerja yang lebih baik dan lebih efisien dibandingkan metode lain dalam klasifikasi penyakit jantung.
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