PENERAPAN METODE MACHINE LEARNING DALAM KLASIFIKASI DATA TEKS PENDERITA KANKER
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Abstract
Cancer is a condition characterized by the uncontrolled growth of abnormal cells that can invade various organs of the body. This research aims to compare cancer classification models utilizing Random Forest, XGBoost, and Support Vector Machine (SVM) methods, with accuracy results reported for each algorithm. The dataset used consists of 7569 rows with two columns, one of which is the classification target, "Target". Before being used in the study, data preprocessing was conducted to improve data quality. The results revealed that Random Forest and XGBoost achieved the highest accuracy of 100%, while SVM achieved 92% accuracy respectively. This research not only compares the accuracy of the algorithms, but also explores the most common cancer types of these three cancers. Through this research, it is hoped to provide further insight into the effectiveness of classification algorithms in cancer patient identification as well as highlighting the types of cancer with the highest number of cases.
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