Pengaruh Reduksi Fitur Pada Klasifikasi Kanker Paru Menggunakan CNN Dengan Arsitektur GoogLeNet
DOI:
https://doi.org/10.14421/fourier.2023.121.20-32Keywords:
Kanker Paru, CNN, GoogLeNet, Reduksi Fitur, PCAAbstract
Kanker paru merupakan jenis kanker dengan penyebab kematian terbanyak. Penelitian ini bertujuan untuk mengklasifikasikan jenis kanker paru apakah termasuk kedalam kelas lung adenocarcinoma, benign lung tissue, lung squamous cell carcinoma berdasarkan citra histopatologi menggunakan metode CNN arsitektur GoogLeNet serta reduksi fitur PCA. Evaluasi model yang digunakan pada penelitian ini menggunakan confusion matrix. Data yang digunakan dalam penelitian ini sejumlah 15000 data yang terbagi menjadi 3 kelas dengan masing-masing kelas berjumlah 5000 data. Pada penelitian ini parameter uji coba yang digunakan yaitu probabilitas dropout dan jumlah batchsize. Lalu, metode reduksi fitur yang digunakan yaitu PCA. Hasil terbaik yang diperoleh yaitu pada pembagian data 90:10 dengan nilai probabilitas dropout 0.9 dan jumlah batchsize 8 dengan memperoleh nilai akurasi, sensitivitas, spesifitas berturur-turut yaitu 99.95%, 99.97%, dan 99.86% serta membutuhkan waktu training selama 93 menit 27 detik.
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