Prediksi Kanker Darah Menggunakan Metode Convolutional Neural Network
DOI:
https://doi.org/10.54082/jiki.156Kata Kunci:
Convolutional Neural Network (CNN), Deteksi Dini, Kanker Darah, Leukemia, Mobilenetv2Abstrak
Leukemia pada anak-anak di Indonesia menjadi perhatian serius dalam bidang kesehatan karena variasi tingkat kelangsungan hidup dan dampak negatif dari pengobatan. Penelitian ini bertujuan meningkatkan deteksi dini dan pengelolaan leukemia pada anak-anak melalui penerapan metode Convolutional Neural Network (CNN). Metode yang digunakan adalah arsitektur MobileNetV2 untuk mengklasifikasikan gambar sel darah terkait kanker darah. Dataset yang digunakan berisi 3257 gambar sel darah yang telah dipreproses menjadi resolusi 300x300 piksel. Hasil penelitian menunjukkan bahwa implementasi CNN dengan arsitektur MobileNetV2 menghasilkan akurasi 95.6%, presisi 94.8%, recall 96.2%, dan F1-score 95.5%. Evaluasi model menggunakan confusion matrix menunjukkan tingkat kesalahan yang rendah dalam klasifikasi gambar normal dan leukemia, menyoroti efisiensi dan efektivitas MobileNetV2 dalam klasifikasi gambar medis.
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