Klasifikasi Tumor Payudara Jinak dan Ganas Menggunakan Algoritma C4.5 pada Dataset WBCD
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Abstract
Kanker payudara merupakan salah satu penyebab utama kematian pada wanita di seluruh dunia dengan lebih dari 2 juta kasus baru setiap tahunnya. Deteksi dini melalui klasifikasi tumor yang akurat sangat penting untuk meningkatkan tingkat kelangsungan hidup pasien. Tujuan penelitian ini adalah mengimplementasikan dan mengevaluasi algoritma C4.5 dalam mengklasifikasikan tumor payudara menjadi jinak (benign) atau ganas (malignant) menggunakan Wisconsin Breast Cancer Diagnostic dataset. Dataset terdiri dari 569 sampel dengan 30 fitur numerik yang diekstrak dari citra Fine Needle Aspiration (FNA). Data dibagi menjadi 70% training set (398 sampel) dan 30% test set (171 sampel) dengan stratified sampling. Model dievaluasi menggunakan 10-fold cross-validation dan diukur dengan metrik accuracy, precision, recall, F1-score, specificity, dan kappa statistic. Penelitian ini menghasilkan nilai akurasi sebesar 90.06% pada test set dengan sensitivity 89.06% dan specificity 90.65%. Kappa statistic sebesar 0.790 menunjukkan substantial agreement. Dari 171 sampel uji, model berhasil mengidentifikasi 57 dari 67 kasus malignant (precision 85.07%) dan 97 dari 104 kasus benign (NPV 93.27%). F1-score mencapai 87.02%. Algoritma C4.5 terbukti efektif sebagai screening tool dengan performa excellent, menghasilkan decision rules yang interpretable untuk mendukung keputusan klinis, namun memerlukan konfirmasi medis terutama untuk prediksi dengan confidence rendah dan implementasi protokol follow-up ketat untuk mitigasi risiko false negative.
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