eprintid: 76836
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/68/36
datestamp: 2026-06-19 07:12:29
lastmod: 2026-06-19 07:12:29
status_changed: 2026-06-19 07:12:29
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Jamila Maulida Sholichati, NIM.: 22106010009
title: ANALISIS KINERJA ALGORITMA DECISION TREE DAN RANDOM FOREST PADA KLASIFIKASI MULTIKELAS CUITAN X MENGGUNAKAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)  (STUDI KASUS: DATA CUITAN TERKAIT GRUP K-POP AESPA)
ispublished: pub
subjects: 515.6
divisions: jur_mat
full_text_status: restricted
keywords: Klasifikasi Teks, Decision Tree, Random Forest, TF-IDF, Media Sosial X, Multikelas
note: Prof. Dr. Dra. Hj. Khurul Wardati, M.Si. dan Muhamad Rashif Hilmi, S.Si., M.Sc.
abstract: Text classification is the process of grouping text data into specific categories based on the characteristics of the words or language patterns they contain. User activity on social media platform X generates a large amount of unstructured text data, necessitating a classification method to identify the content types of tweets. Tweets related to the K-pop group aespa were classified into four categories: Information, Opinion/Expression, Fandom Interaction, and Promotion using the Decision Tree and Random Forest algorithms with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation. The research dataset consisted of 2,304 tweets scraped and manually labeled. Preprocessing steps included cleaning, tokenization, stopword removal, and stemming, followed by feature extraction using TF-IDF. The evaluation results showed that the Decision Tree algorithm achieved an accuracy of 70%, while the Random Forest algorithm achieved an accuracy of 75%. These results indicate that Random Forest outperformed Decision Tree in the multiclass classification of tweet data related to the group aespa on social media platform X.
date: 2026-06-04
date_type: published
pages: 130
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Jamila Maulida Sholichati, NIM.: 22106010009  (2026) ANALISIS KINERJA ALGORITMA DECISION TREE DAN RANDOM FOREST PADA KLASIFIKASI MULTIKELAS CUITAN X MENGGUNAKAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) (STUDI KASUS: DATA CUITAN TERKAIT GRUP K-POP AESPA).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/76836/1/22106010009_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/76836/2/22106010009_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf