Image of ANALISIS SENTIMEN E-WALLET DI TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE DAN RECURSIVE FEATURE ELIMINATION

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ANALISIS SENTIMEN E-WALLET DI TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE DAN RECURSIVE FEATURE ELIMINATION

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Grouping of positive or negative sentiments in text reviews is increasingly being done automatically for identification. The selection of features in the classification is a problem that is often not solved. Most of the feature selection related to sentiment classification techniques is insurmountable in terms of evaluating significant features that reduce classification performance. Good feature selection technique can improve sentiment classification performance in machine learning approach. First, two sets of customer review data are labeled with sentiment and then retrieved, processed for evaluation. Next, the supports vector machine (svm-rfe) method is created and tested on the dataset. Svm-rfe will be run to measure the importance of the feature by rating the feature iteratively. For sentiment classification, only the top features of the ranking feature sequence will be used. Finally, performance is measured using accuracy, precision, recall, and f1-score. The experimental results show promising performance with an accuracy rate of 81%. This level of reduction is significant in making optimal use of computing resources while maintaining the efficiency of classification performance.

Availability
Inventory Code Barcode Call Number Location Status
2107002235T54677T546772021Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T546772021
Publisher
Inderalaya : Fakultas Ilmu komputer, Magister Ilmu Komputer.,
Collation
66 hlm,: ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.754 07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Prodi Magister Ilmu Komputer
Situs Jejaring Sosial, Sosial Media-Twitter
Specific Detail Info
-
Statement of Responsibility
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No other version available

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