Perbandingan Klasifikasi Penyakit Kanker Paru-Paru menggunakan Support Vector Machine dan K-Nearest Neighbor
DOI:
https://doi.org/10.33998/processor.2023.18.1.700Kata Kunci:
K-Fold Cross Validation, K-Nearest Neighbor, Lung Cancer, Percentage Split, Support Vector MachineAbstrak
termasuk ke dalam penyebab pertama kematian pada pria dan menjadi penyebab kedua kematian pada wanita. Salah satu cara untuk mengurangi tingkat kematian karena kanker paru-paru adalah dengan melakukan deteksi dini, yakni dengan klasifikasi. Proses mengidentifikasi dan mengelompokkan objek dengan ciri atau karakteristik yang sama ke dalam beberapa kelas yang telah ditentukan disebut dengan klasifikasi. Beberapa algoritma yang banyak digunakan dalam proses klasifikasi adalah Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). SVM memiliki kelebihan, yakni mampu mengidentifikasi hyperplane secara terpisah sehingga memaksimalkan margin antara dua kelas atau lebih yang berbeda, tetapi sulit digunakan dalam data yang berukuran besar, sedangkan KNN dapat melakukan pemisahan data yang berskala besar dan tangguh terhadap noise pada data. Penelitian ini bertujuan untuk membangun model dengan menggunakan algoritma SVM dan KNN pada klasifikasi penyakit kanker paru-paru. Dataset penyakit kanker paru-paru memiliki jumlah data sebanyak 309 data dimana data dibagi dengan menggunakan metode percentage split dan k-fold cross validation pada masing-masing algoritma yang digunakan. Parameter yang digunakan dalam mengevaluasi model adalah akurasi, presisi, dan recall. Dari penelitian yang dilakukan, nilai akurasi, presisi, dan recall tertinggi diperoleh pada algoritma SVM metode percentage split dengan nilai secara berturut-turut, yakni 95,16%, 88%, dan 82,5%. Hal tersebut mengindikasikan bahwa algoritma SVM dengan metode percentage split memiliki performa yang lebih baik dalam melakukan klasifikasi penyakit kanker paru-paru dibandingkan algoritma dan metode lainnya
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