Klasifikasi Penyakit Jantung Menggunakan Random Forest
DOI:
https://doi.org/10.29407/dxbyba25Keywords:
Klasifikasi, Penyakit Jantung, Random Forest, Machine Learning, Data Klinis, StreamlitAbstract
Penyakit jantung merupakan salah satu penyebab utama kematian di dunia sehingga diperlukan metode deteksi dini yang akurat dan efisien. Penelitian ini bertujuan untuk mengklasifikasikan risiko penyakit jantung menggunakan algoritma Random Forest berdasarkan data klinis pasien. Dataset yang digunakan adalah Heart Failure Clinical Records Dataset yang berisi atribut medis seperti usia, tekanan darah, fraksi ejeksi, kadar serum kreatinin, serta riwayat penyakit penyerta. Tahapan penelitian meliputi pengumpulan data, praprosessing data, pembagian data menjadi data latih dan data uji, pelatihan model Random Forest, evaluasi model, serta implementasi sistem dalam bentuk aplikasi berbasis web menggunakan Streamlit. Proses praprosessing dilakukan untuk meningkatkan kualitas data melalui penanganan nilai hilang, pembersihan data, encoding variabel kategorikal, normalisasi, dan balancing dataset. Hasil evaluasi menunjukkan bahwa model Random Forest memiliki performa yang baik dengan nilai accuracy, precision, recall, dan F1-score yang seimbang, serta mampu meminimalkan kesalahan false negative yang sangat penting dalam konteks kesehatan. Implementasi model ke dalam aplikasi Streamlit memungkinkan pengguna melakukan prediksi risiko penyakit jantung secara interaktif dan real-time. Dengan demikian, penelitian ini menunjukkan bahwa algoritma Random Forest efektif dan layak digunakan sebagai sistem pendukung keputusan dalam deteksi dini penyakit jantung.
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