Literatur Review: Klasifikasi Penyakit Parasit dengan Algoritma Decision Tree dan K-Nearest Neighbors (KNN)

Abstract

This literature review discusses the classification of parasitic diseases using Decision Tree and K-Nearest Neighbors (KNN) algorithms. Parasitic diseases, which are commonly found in tropical areas, require accurate diagnosis to prevent their spread and improve the effectiveness of treatment. In recent decades, Decision Tree and KNN algorithms have been widely used in medical data classification, especially for disease diagnosis. This study aims to evaluate the effectiveness of these two algorithms in parasitic disease classification based on a recent literature review. The literature review method was carried out by collecting and analyzing five related articles in the last five years. The results show that both algorithms have their own advantages and disadvantages; KNN excels in accuracy on large datasets while Decision Tree provides easier interpretation of results. The main challenges in using these two algorithms involve parameter selection and data sensitivity. Further recommendations in this study include the use of ensemble techniques to combine the advantages of both algorithms.