Perbandingan Algoritma K-NN, SVM, dan Decision Tree dalam Klasifikasi Kelenjar Tiroid
Abstract
Thyroid disorders are a disease that is dif icult and often misdiagnosed. This is what causes many people to find out too late that they have this thyroid disorder. There are two types of thyroid disorders, namely hyperthyroidism and hypothyroidism. Machine Learning can be utilized to classify these disorders using data mining techniques. Classification is often used to predict many diseases, one of which is thyroid. The aim of this research was to determine the classification of the patient's thyroid. The data used is patient data sourced from Kaggle with 31 features (x) and 3 classes (y), namely 'Negative', 'Hypothyroid' and 'Hyperthyroid'. The data in this study was modeled using the Support Vector Machine (SVM) method with Radial Basis Function (RBF), K-Nearest Neighbor (KNN) and Decision Tree kernels. The results obtained are the percentage accuracy of each algorithm which is 97%, 92% and 91% respectively. From these results it can be concluded that the Support Vector Machine (SVM) algorithm is most suitable to be implemented with this dataset.
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