PERBANDINGAN METODE KLASIFIKASI RANDOM FOREST DAN SUPPORT VECTOR MACHINE TERHADAP DATASET RESIKO KANKER SERVIKS

Abstract

Cervical cancer is a significant global health issue, representing a type of cancer that develops from the cells of the cervix. This research focuses on comparing the effectiveness of two classification methods, namely Random Forest (RF) and Support Vector Machine (SVM), in assessing the risk of cervical cancer. Utilizing relevant datasets, the study aims to identify the strengths and weaknesses of each method and evaluate their ability to provide predictions of cervical cancer risk. Through comparative analysis, it is anticipated that this research will offer valuable insights for the development of more efficient methods for assessing the risk of cervical cancer. The results of this study are expected to contribute to a deeper understanding of the performance comparison between Random Forest and SVM in the context of assessing the risk of cervical cancer, opening opportunities for the optimal application of classification methods in efforts for the prevention and early detection of this disease.