Penerapan Metode Klasifikasi Dengan Algoritma Decision Tree C4.5 Untuk Mendiagnosa Awal Penyakit Ginjal Kronis

Abstract

Patients with kidney disease find it difficult to know because they have to go through a series of laboratory tests with a considerable amount of time at the hospital. The complexity of the detecting process can be made easier using technology with data processing or Data Mining. Data Mining is the process of mining or discovering new information that aims to overcome certain conditions by looking for certain patterns and rules of a large amount of data. To diagnose early patients with chronic kidney disease with Data Mining using the classification method with the Decision Tree C4.5 algorithm. Decision Tree or meaning a decision tree is a prediction model with an hierarchical structure that has the concept of converting data into rules and decision trees, data in decision trees are expressed in tables with attributes and records that state parameters as tree formation criteria. The study used Chronic Kidney Disease data as a dataset and applied the classification method with the Decision Tree C4.5 algorithm. This study uses RapidMiner 9.0.3 data mining tools. The results obtained from this study show an accuracy of 89.05%.