Analisis Sentimen Komentar Youtube Masterchef Indonesia Menggunakan Algoritma Support Vector Machine Dan Gaussian Naive Bayes

Marganingsih, Dirgahayu (2024) Analisis Sentimen Komentar Youtube Masterchef Indonesia Menggunakan Algoritma Support Vector Machine Dan Gaussian Naive Bayes. Undergraduate thesis, Universitas Muhammadiyah Jember.

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Abstract

Social media has become the main platform for sharing information, including video sharing-based platforms such as YouTube, which provides educational, informational and entertainment content for the public. Through the comments feature, users can express opinions and responses, which can then be analyzed to understand the public's views. Sentiment analysis is a method used to analyze text and determine whether the sentiment contained is positive, negative, or neutral. This method is useful for evaluating opinions on a particular issue or object. This research focuses on analyzing public comments regarding the pros and cons of MasterChef Indonesia Season 11 participants winning on YouTube. The data obtained was analyzed using the Support Vector Machine and Gaussian Naïve Bayes algorithms, with a comparison of the performance of the two algorithms. Before applying the oversampling technique for data balancing, the Support Vector Machine algorithm produced 82% accuracy, 88% precision and 72% recall, while Gaussian Naïve Bayes produced 65% accuracy, 52% precision and 81% recall. After the oversampling technique was applied, the performance of the Support Vector Machine increased with 85% accuracy, 84% precision, and 89% recall, while Gaussian Naïve Bayes obtained 72% accuracy, 68% precision, and 72% recall

Contribution

Nama Dosen Pembimbing

NIDN/NIDK

Dosen Pembimbing

Oktavianto, Hardian

NIDN0722108105

Dosen Pembimbing

Abdurrahman, Ginanjar

NIDN0714078704

Item Type:	Thesis (Undergraduate)
Keywords/Kata Kunci:	Sentiment Analyst, Support Vector Machine, Gaussian Naïve Bayes
Subjects:	000 Computer Science, Information, & General Works > 004 Data Processing, Computer Science
Divisions:	Faculty of Engineering > Department of Informatics Engineering (S1)
Depositing User:	Dirgahayu Marganingsih | dirgahayumrg@gmail.com
Date Deposited:	30 Jan 2025 02:47
Last Modified:	30 Jan 2025 02:47
URI:	http://repository.unmuhjember.ac.id/id/eprint/23871

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