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      "abstract": "Penyakit jantung merupakan salah satu penyebab utama kematian dengan angka\r\nprevalensi yang terus meningkat dan berdampak signifikan terhadap kualitas hidup\r\nmasyarakat serta beban sistem kesehatan. Keterlambatan dalam mendeteksi risiko\r\npenyakit jantung menyebabkan penanganan medis sering dilakukan pada tahap\r\nlanjut, sehingga diperlukan pendekatan prediktif yang mampu mengidentifikasi\r\nrisiko secara dini dan akurat berbasis data klinis dan demografis. Penelitian ini\r\nbertujuan untuk merancang dan mengimplementasikan model Hybrid Gradient\r\nBoosting berbasis kombinasi algoritma XGBoost dan LightGBM guna\r\nmengoptimalkan performa prediksi risiko penyakit jantung. Tahapan penelitian\r\nmeliputi pengumpulan dataset Heart Attack Indonesia, preprocessing data,\r\nexploratory data analysis (EDA), feature engineering, pembagian data latih dan data\r\nuji, pelatihan model XGBoost dan LightGBM, serta evaluasi kinerja model\r\nmenggunakan metrik akurasi, precision, recall, dan F1-score. Selain itu, dilakukan\r\ncross-validation untuk menguji kemampuan generalisasi model serta analisis\r\nfeature importance dan SHAP untuk meningkatkan interpretabilitas model. Hasil\r\npenelitian menunjukkan bahwa pendekatan Hybrid Gradient Boosting mampu\r\nmenghasilkan performa prediksi yang optimal, stabil, dan memiliki kemampuan\r\ngeneralisasi yang baik. Variabel usia, tekanan darah, kadar kolesterol, dan indeks\r\nmassa tubuh (BMI) terbukti berpengaruh signifikan terhadap risiko penyakit\r\njantung. Penelitian ini berkontribusi dalam pengembangan model prediksi yang\r\nakurat dan dapat diinterpretasikan, serta dapat dimanfaatkan oleh tenaga medis,\r\ninstitusi kesehatan, dan peneliti sebagai alat bantu pengambilan keputusan dan\r\npengembangan sistem pendukung diagnosis penyakit jantung.",
      "dir": "disk0\/00\/03\/22\/37",
      "title": "OPTIMASI PREDKSI RISIKO PENYAKIT JANTUNG\r\nMENGGUNAKAN METODE HYBRID GRADIENT BOOSTIG  \r\nBERBASIS KOMBINASI MODEL XGBOOST DAN LIGHTGBM",
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