relation: https://digilib.uin-suka.ac.id/id/eprint/76836/
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)
creator: Jamila Maulida Sholichati, NIM.: 22106010009
subject: 515.6 Metode Analitik - Matematika
description: 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
type: Thesis
type: NonPeerReviewed
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/76836/1/22106010009_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/76836/2/22106010009_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf
identifier:   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.