%A NIM.: 19106010017 Rifda Nida’ul Labibah
%O Pembimbing: Dr. Epha Diana Supandi S.Si., M.Sc dan Muhamad Rashid Hilmi, S.Si., M.Sc.
%T PERBANDINGAN ALGORITMA C5.0 DAN ALGORITMA CLASSIFICATION AND REGRESSION TREE (CART) PADA DECISION TREE UNTUK KLASIFIKASI DATA  (STUDI KASUS : TINGKAT PENGANGGURAN TERBUKA INDONESIA MENURUT KABUPATEN/KOTA TAHUN 2020)
%X Data classification is an important part of data analysis which aims to find similarities in data characteristics that will be grouped into a class. One method for classifying data is the decision tree method. To form a decision tree there are various algorithms that can be used including C5.0 and Classification and Regression Tree (CART). The C5.0 algorithm uses the calculation of the gain value and gain ratio. While the CART algorithm uses the gini index calculation. The C5.0 and CART algorithms were applied to a case study of Indonesia's open unemployment rate by district/city in 2020. The results obtained were an accuracy rate with the C5.0 algorithm of 64,009% while the accuracy rate with the CART algorithm was 60,019%. So that it can be said that the C5.0 algorithm is the best method for classifying the open unemployment rate in Indonesia compared to the CART algorithm.
%K Klasifikasi, Decision Tree, Algoritma C5.0, Algoritma Classification and Regression Tree (CART), Tingkat Pengangguran Terbuka
%D 2023
%I UIN SUNAN KALIJAGA YOGYAKARTA
%L digilib59019