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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)
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.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
NIM.: 22106010009 Jamila Maulida Sholichati
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) (Text)
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) (Other)
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