Optimasi Random Forest untuk Prediksi Penyakit Jantung Menggunakan SMOTEENN dan Grid Search
DOI:
https://doi.org/10.52436/1.jpti.855Kata Kunci:
Penyakit Jantung, Random Forest, SMOTEENN, GridSearchCV, klasifikasi medisAbstrak
Penyakit jantung merupakan salah satu penyebab utama kematian di dunia, dengan sekitar 17,9 juta kematian setiap tahun. Diagnosis dini dan akurat sangat penting untuk pengobatan yang efektif, namun ketidakseimbangan kelas dalam dataset medis sering menyebabkan bias pada model prediktif, khususnya dalam mengidentifikasi pasien dengan penyakit jantung (kelas minoritas). Studi ini bertujuan untuk mengoptimalkan kinerja algoritma Random Forest dalam memprediksi penyakit jantung dengan mengatasi ketidakseimbangan data menggunakan teknik SMOTEENN (Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors) serta penyetelan hiperparameter melalui GridSearchCV. Dataset dibagi menjadi data pelatihan (80%) dan pengujian (20%), dengan evaluasi kinerja menggunakan metrik akurasi, presisi, recall, spesifisitas, F1-score, dan AUC ROC. Hasil penelitian menunjukkan bahwa model yang dioptimalkan mencapai akurasi sebesar 94%, presisi 87%, recall 100%, spesifisitas 91%, F1-score 93%, dan AUC sebesar 0,99. Teknik SMOTEENN terbukti efektif dalam meningkatkan representasi kelas minoritas tanpa menimbulkan noise yang signifikan, sementara GridSearchCV berhasil menemukan kombinasi hiperparameter terbaik untuk meningkatkan performa model. Model Random Forest yang dihasilkan menunjukkan potensi tinggi sebagai alat bantu diagnosis dini penyakit jantung, yang dapat berkontribusi dalam menurunkan angka kematian dan meningkatkan efisiensi biaya perawatan.
Unduhan
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