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      "keywords": "Diabetes Mellitus, Machine Learning, SMOTE-ENN, Random Forest,\r\nClassification.",
      "department": "Fakultas Ilmu Komputer",
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      "date": "2026-02-03",
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            "given": "Fransiskus Wilis Alit",
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      "type": "thesis",
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      "title": "ANALISIS DETEKSI PENYAKIT DIABETES DENGAN\r\nKELAS YANG TIDAK SEIMBANG DITANGANI\r\n TEKNIK SMOTE-ENN  PADA ALGORITMA \r\nMACHINE LEARNING",
      "abstract": "Diabetes melitus merupakan salah satu penyakit kronis dengan prevalensi\r\ntinggi yang dapat menimbulkan berbagai komplikasi serius apabila tidak terdeteksi\r\nsejak dini. Penerapan machine learning dalam deteksi diabetes sering menghadapi\r\npermasalahan ketidakseimbangan kelas (class imbalance), di mana jumlah data\r\npenderita diabetes jauh lebih sedikit dibandingkan data non-diabetes, sehingga\r\nmenurunkan kemampuan model dalam mengenali kelas minoritas. Penelitian ini\r\nbertujuan untuk menganalisis efektivitas teknik SMOTE-ENN (Synthetic Minority\r\nOver-sampling Technique with Edited Nearest Neighbors) dalam menangani\r\nketidakseimbangan kelas serta membandingkan kinerja beberapa algoritma\r\nmachine learning dalam mendeteksi penyakit diabetes. Dataset yang digunakan\r\nadalah Behavioral Risk Factor Surveillance System (BRFSS) tahun 2015 yang\r\nterdiri dari 253.680 data dengan 22 variabel indikator kesehatan. Tahapan\r\npenelitian meliputi preprocessing data, penerapan SMOTE-ENN, pelatihan model\r\nmenggunakan algoritma Logistic Regression, K-Nearest Neighbors, Random\r\nForest, dan XGBoost, serta evaluasi model menggunakan metrik accuracy,\r\nprecision, recall, F1-score, dan AUC-ROC. Hasil penelitian menunjukkan bahwa\r\npenerapan SMOTE-ENN mampu meningkatkan performa model dalam mendeteksi\r\nkelas minoritas diabetes. Berdasarkan hasil evaluasi, algoritma Random Forest\r\nmemberikan performa terbaik dengan nilai akurasi sebesar 95,85%, sehingga\r\ndinilai paling efektif dalam mendeteksi penyakit diabetes. Model terbaik\r\nselanjutnya diimplementasikan dalam bentuk aplikasi web berbasis Flask untuk\r\nmembantu prediksi risiko diabetes secara interaktif.",
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