IMPLEMENTASI ENSEMBLE MODEL UNTUK PREDIKSI FAKTOR RESIKO KANKER SERVIKS DENGAN KOMBINASI ALGORITMA MACHINE LEARNING

• ANDICHA VEBIYATAMA
• 14230014

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ABSTRAK

ABSTRAK

Penelitian ini mengusulkan tiga model ensemble berbasis voting untuk prediksi faktor risiko kanker serviks menggunakan dataset UCI Cervical Cancer (Risk Factors). Model yang dikembangkan adalah Voting (XGB+KNN+LR), Voting (ADA+RF+SGD), dan Voting (CAT+SVM+NB). Pipeline penelitian mencakup KNN Imputer untuk menangani missing value, PCA untuk reduksi dimensi, dan SMOTE untuk mengatasi ketidakseimbangan kelas. Evaluasi dilakukan menggunakan crossvalidation dan pengujian akhir dengan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa dua model pertama mencapai performa sempurna (100%) pada seluruh metrik pengujian, sedangkan model ketiga mencatatkan akurasi 99,61%, presisi 100%, recall 93,33%, dan F1-score 96,55%. Selain itu, kalibrasi probabilitas menunjukkan nilai Brier Score 0,012, menandakan reliabilitas prediksi yang tinggi. Temuan ini melampaui capaian penelitian sebelumnya (maksimal 99,99%), sehingga dapat diklaim sebagai state-of-the-art untuk prediksi kanker serviks pada dataset ini

KATA KUNCI

ALGORITMA MACHINE LEARNING

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DAFTAR PUSTAKA

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