ANALISIS SENTIMEN ULASAN RESTORAN PADA TRIPADVISOR MENGGUNAKAN MERTODE SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

FEBRYNO, NOUVALDHA DIMAS and Utami, Alvi Syahrini and Saputra, Danny Matthew (2024) ANALISIS SENTIMEN ULASAN RESTORAN PADA TRIPADVISOR MENGGUNAKAN MERTODE SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

Tripadvisor is a website that provides information about restaurants, user-generated restaurant reviews.data in the form of comment text. However, there are many features in review makes textual data ambiguous, thus causing difficulties sentiment analysis. To overcome this challenge, this research uses Information Gain feature selection method to reduce high features dimensions in sentiment analysis of Tripadvisor restaurant reviews. Exam The research results show that implementing the Information Gain feature selection method in the linear SVM kernel algorithm with a parameter C value 1 produces the highest performance. Results accuracy, precision, recall, and f-measure are 0.89, 0.89, 0.77, and 0.8, every. Next use this feature selection approach reduced the number of features and computing time from 12,198 to 2885 features and computing time of just 0.8 seconds. Besides that, The use of the SVM algorithm without feature selection produces inferior performance with accuracy 0.87, precision 0.89, recall of 0.74, f-measure of 0.79, and computing time of 0.98 seconds, considering a total of 12,198 features. These results show that accurate parameter selection and application of Information Obtaining feature selection methods can improve efficiency, effectiveness, and accuracy of sentiment analysis. This research seeks to improving sentiment analysis methods on large text data a number of features.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Support Vector Machine (SVM), Information Gain
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nouvaldha Dimas Febryno
Date Deposited:	16 Aug 2024 04:32
Last Modified:	16 Aug 2024 04:32
URI:	http://repository.unsri.ac.id/id/eprint/155420

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