@phdthesis{digilib76836,
           month = {June},
           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)},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22106010009 Jamila Maulida Sholichati},
            year = {2026},
            note = {Prof. Dr. Dra. Hj. Khurul Wardati, M.Si. dan Muhamad Rashif Hilmi, S.Si., M.Sc.},
        keywords = {Klasifikasi Teks, Decision Tree, Random Forest, TF-IDF, Media Sosial X, Multikelas},
             url = {https://digilib.uin-suka.ac.id/id/eprint/76836/},
        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.}
}