KLASIFIKASI PENYAKIT KANKER PAYUDARA PADA CITRA MAMMOGRAM MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DAN RANDOM FOREST
DOI:
https://doi.org/10.22437/jop.v10i3.43794Keywords:
convolutional neural network, citra mammogram, kanker payudara, random forestAbstract
Penelitian ini membahas klasifikasi kanker payudara pada citra mammogram menggunakan algoritma Convolutional Neural Network (CNN) dan Random Forest dengan implementasi berbasis Python. Tujuan penelitian ini adalah membandingkan performa algoritma CNN dan Random Forest dalam mengklasifikasikan kanker payudara pada citra mammogram, yang dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Dataset citra mammogram diperoleh dari platform Kaggle, mencakup dua kategori: tumor jinak (benign) dan tumor ganas (malignant). Tahap preprocessing data meliputi reduksi noise, normalisasi intensitas piksel, dan ekstraksi fitur. Model CNN dan Random Forest dilatih dan dievaluasi menggunakan metrik performa yang telah ditentukan, dengan pembagian dataset 80% data latih dan 20% data uji untuk memastikan evaluasi objektif. Hasil penelitian menunjukkan bahwa model CNN memiliki akurasi 97%, presisi 90%, recall 94%, dan F1-score 92%, sedangkan Random Forest mencapai akurasi 95%, presisi 86%, recall 91%, dan F1-score 88%. Hasil ini mengindikasikan bahwa model CNN menunjukkan performa lebih unggul dan berpotensi sebagai sistem pendukung diagnosis kanker payudara.
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Copyright (c) 2025 Adelia Latifah, Alrizal Alrizal, Yoza Fendriani
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