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      "keywords": "Diabetes, Imblanaced data, Sampling, Random Forest, XGBoost",
      "institution": "Universitas AMIKOM Yogyakarta",
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      "abstract": "Ketidakseimbangan kelas (imbalanced data) merupakan permasalahan \r\nutama dalam klasifikasi data medis, khususnya ada kasus penyakit diabetes.\r\nKondisi ini menyebabkan model klasifikasi cenderung lebih fokus pada kelas\r\nmayoritas, sehingga kemampuan dalam mendeteksi pasien diabetes sebagai kelas\r\nminoritas menjadi kurang optimal. Apabila tidak ditangani dengan tepat,\r\npermasalahan tersebut dapat menunrunkan kualitas sistem pendukung keputusan\r\ndibidang Kesehatan.\r\nPenelitian ini bertujuan untuk menganalisis pengaruh berbagai metode\r\nsampling dalam meningkatkan kinerja model klasifikasi diabetes. Metode yang\r\ndigunakan adalam Random Fores dan XGBoost dengan menggunakan beberapa\r\npendekatan penanganan ketidakseimbangan kelas, yaitu SMOTE, ROS,\r\nSMOTENN, ADASYN, RUS, ClusterCentroids, Tomek Links, dan ENN. Evaluasi\r\nkinerja model dilakukan menggunakan metrik akurasi dan F1-score guna menilai\r\nkeseimbangan performa dalam mengklasifikasikan kelas mayoritas dan minoritas.\r\nHasil penelitian menunjukan bahwa metode Tomek Links memberikan\r\npeforma yang paling konsisten pada kedua model. Pada model XGBoost, metode\r\nini mampu meningkatkan nilai F1-score diabanding data asli, sedangkan pada\r\nmodel Rndom Forest performanya relatif stabil dengan perbedaan yang tidaak\r\nsignifikan. Penelitian ini memberikan konstribusi dalam pemeilihan metode\r\nsampling yang efektif untuk klasifikasi data medis. Hasil penelitian dapat\r\ndimanfaatkan oleh peneliti, praktisi data, dan institusi Kesehatan sebagai\r\nreferensi dalam pengembangan system klasifikasi penyakit berbasis pembelajaran\r\nmesin.",
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