IMPLEMENTASI TEXT MINING PADA ANALISIS SENTIMEN PEMAIN NATURALISASI TIMNAS INDONESIA DENGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM)

Tri Widodo, Fajar (2025) IMPLEMENTASI TEXT MINING PADA ANALISIS SENTIMEN PEMAIN NATURALISASI TIMNAS INDONESIA DENGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM). S1 thesis, Universitas Muhammadiyah Ponorogo.

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Abstract

Football is the most popular and widely played sport worldwide, including in Indonesia. FIFA is the highest governing body for all football matters. FIFA has numerous football competitions on its agenda, including the World Cup. As a member of FIFA, the Indonesian Football Association (PSSI) has the opportunity to participate in FIFA tournaments. The World Cup remains a dream for all football fans in the country. One of the Indonesian Football Federation's (PSSI) strategies to improve the national team's performance is through a foreign player naturalization program. The program's goal is to strengthen the national team's performance so it can compete for a place in the 2026 World Cup. The naturalization program has sparked various public responses, particularly through Twitter. Public opinion expressed through this platform can be analyzed to determine public sentiment towards the naturalization policy. This study aims to conduct sentiment analysis on Twitter users' tweets regarding naturalized Indonesian national team players using the Naïve Bayes and Support Vector Machine (SVM) methods. The analysis process includes data collection, pre-processing, and sentiment classification into two categories: positive and negative. The results show that Naïve Bayes has a higher level of accuracy than Support Vector Machine. The accuracy of Naïve Bayes is 76.23%, while SVM has an accuracy level of 70.29%. In addition, the Naïve Bayes model is also superior in precision, recall, and f1-score values compared to SVM. Naïve Bayes has a value of 78.43% for precision, 75.47% for recall, and 76.93% for f1-score. Meanwhile, the SVM achieved a precision of 76.74%, a recall of 62.26%, and an f1-score of 68.73%. Based on these results, it can be concluded that the Naïve Bayes method is more effective in classifying public sentiment toward the naturalization program for Indonesian national team players.

Item Type:	Thesis (S1)
Uncontrolled Keywords:	Analisis Sentimen, Timnas Indonesia, Naturalisasi, Naïve Bayes, Support Vector Machine (SVM), Evaluasi Model
Subjects:	L Education > L Education (General)
Divisions:	Faculty of Engineering
Depositing User:	Fajar Tri Widodo
Date Deposited:	04 Nov 2025 02:26
Last Modified:	04 Nov 2025 02:26
URI:	https://eprints.umpo.ac.id/id/eprint/17898

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