ANALYSIS OF PUBLIC SENTIMENT TOWARDS MAJOR TEDDY INDRA WIJAYA ON TWITTER SOCIAL MEDIA USING THE LOGISTIC REGRESSION ALGORITHM

SINAGA, ANTONIUS STEVEN DIMAS PRASETYA (2026) ANALYSIS OF PUBLIC SENTIMENT TOWARDS MAJOR TEDDY INDRA WIJAYA ON TWITTER SOCIAL MEDIA USING THE LOGISTIC REGRESSION ALGORITHM. Tugas Akhir thesis, University of Technology Yogyakarta.

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Abstract

ABSTRACT This research aims to analyze public sentiment toward Major Teddy Indra Wijaya, an aide to Prabowo Subianto, using the Logistic Regression algorithm. Sentiment analysis is essential because public figures like Major Teddy, who are closely associated with prominent national political leaders, can significantly influence public perception. Understanding this sentiment is crucial for identifying both positive and negative public responses, which can serve as indicators of the public’s perception of Prabowo Subianto’s political image. This study uses data collected from the social media platform Twitter via web crawling, covering the period from January 2024 to the time this report was prepared. The primary challenge is how to effectively apply the Logistic Regression algorithm to map public sentiment from complex, diverse textual data. The research process includes data collection, preprocessing, labelling, model training, and evaluating model performance using metrics such as accuracy, precision, recall, and F1 score. The results indicate that the Logistic Regression model achieved 73.68% accuracy, 75% precision, 71% recall, and an F1 score of 0.73. These findings demonstrate strong predictive performance for public sentiment. It is hoped that this research will advance sentiment analysis methods in a socio-political context and provide strategic insights for managing public communications and reputation in the digital era. Keywords: Sentiment Analysis, Logistic Regression, Social Media, Twitter

Item Type:	Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects:	T Technology > T Technology (General) > T201 Patents. Trademarks
Divisions:	Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User:	Kaprodi S1 Informatika UTY
Date Deposited:	05 May 2026 03:21
Last Modified:	05 May 2026 03:21
URI:	http://eprints.uty.ac.id/id/eprint/19773

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