METODE ENSEMBLE UNTUK MENGATASI DATA TIDAK SEIMBANG PADA PREDIKSI KANKER SERVIKS

Yudhistira Yudhistira, Suhardjono Suhardjono, Imam Nawawi, Ahmad Hafidzul Kahfi, Febri Ainun Jariyah

Abstract


Kanker serviks merupakan salah satu penyakit dengan tingkat mortalitas yang tinggi pada perempuan, sehingga deteksi dini menjadi sangat penting dalam upaya pencegahan dan penanganan klinis. Penerapan machine learning pada prediksi kanker serviks sering menghadapi permasalahan ketidakseimbangan kelas, di mana jumlah data pasien kanker jauh lebih sedikit dibandingkan non-kanker, yang berpotensi menurunkan kemampuan model dalam mendeteksi kasus positif. Penelitian ini bertujuan untuk mengevaluasi efektivitas pendekatan ensemble learning khusus data tidak seimbang, yaitu Balanced Random Forest, EasyEnsemble, dan RUSBoost, dalam meningkatkan performa prediksi risiko kanker serviks. Evaluasi dilakukan menggunakan Stratified K-Fold Cross Validation dengan lima fold dan metrik akurasi, precision, recall, F1-score, serta Area Under the ROC Curve (AUC-ROC). Hasil eksperimen menunjukkan bahwa Balanced Random Forest memberikan performa terbaik dengan nilai akurasi sebesar 95.46%, recall 87.27%, F1-score 71.12%, dan AUC-ROC tertinggi sebesar 0.9466. EasyEnsemble menghasilkan akurasi sebesar 95,69% dengan F1-score tertinggi sebesar 72,18% dan AUC-ROC 0,9304, sedangkan RUSBoost menunjukkan sensitivitas yang baik terhadap kelas minoritas dengan nilai recall sebesar 81.82% dan AUC-ROC 0.9229. Hasil penelitian ini membuktikan bahwa pendekatan ensemble khusus untuk data tidak seimbang mampu meningkatkan kemampuan deteksi kelas minoritas secara signifikan dan lebih efektif dibandingkan pendekatan klasifikasi konvensional pada prediksi risiko kanker serviks

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DOI: http://dx.doi.org/10.36723/juri.v18i1.822

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