eprintid: 59019
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/05/90/19
datestamp: 2023-05-31 06:55:45
lastmod: 2023-05-31 06:55:45
status_changed: 2023-05-31 06:55:45
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Rifda Nida’ul Labibah, NIM.: 19106010017
title: 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)
ispublished: pub
subjects: Matematika
divisions: jur_mat
full_text_status: restricted
keywords: Klasifikasi, Decision Tree, Algoritma C5.0, Algoritma Classification and Regression Tree (CART), Tingkat Pengangguran Terbuka
note: Pembimbing: Dr. Epha Diana Supandi S.Si., M.Sc dan Muhamad Rashid Hilmi, S.Si., M.Sc.
abstract: 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.
date: 2023-03-30
date_type: published
pages: 140
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Rifda Nida’ul Labibah, NIM.: 19106010017  (2023) 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).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/59019/1/19106010017_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/59019/2/19106010017_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf