Image of ANALISIS SENTIMEN REVIEW MOVIE PADA IMDB MENGGUNAKAN METODE SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). 

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ANALISIS SENTIMEN REVIEW MOVIE PADA IMDB MENGGUNAKAN METODE SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). 

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IMDb is a website that provides information about all movies, including user-generated movie reviews. Reviews are identified through textual data in the form of comment text. However, the large number of features in reviews makes the textual data ambiguous, creating difficulties for sentiment analysis. To address this challenge, this research employs the Information Gain feature selection method to reduce the high feature dimensions in sentiment analysis of IMDb movie reviews. The test results indicate that implementing the Information Gain feature selection method within a linear kernel SVM algorithm with a parameter C value of 1 yields the highest performance. The resulting accuracy, precision, recall, and f-measure are 0.88, 0.88, 0.87, and 0.87, respectively. Furthermore, utilizing this feature selection approach reduces the number of features and computation time from 21,989 to 5,869 features and only 0.12 seconds of computation time. In contrast, the use of the SVM algorithm without feature selection resulted in inferior performance with an accuracy of 0.83, precision of 0.84, recall of 0.84, f-measure of 0.83, and a computation time of 2.25 seconds, considering a total of 21989 features. These outcomes indicate that accurate parameter selection and the application of the Information Gain feature selection method can enhance the efficiency, effectiveness, and accuracy of sentiment analysis. This study seeks to enhance methods for sentiment analysis on text data with a large number of features.

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Inventory Code Barcode Call Number Location Status
2307006420T130885T1308852023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1308852023
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xv, 95 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Teknik Informatika
Pemrosesan Data Analisis Sentimen
Specific Detail Info
-
Statement of Responsibility
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No other version available

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