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PENGARUH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) PADA ANALISIS SENTIMEN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

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Imbalanced data is a problem that often occurs when conducting research in the field of sentiment analysis. This problem occurs when the dataset being analyzed has more positive classes than negative classes or vice versa so that the terms majority and minority data appear. If the majority data is more dominant then the classification process tends to produce a classification into the majority class. This causes the need for a solution to overcome the problem of imbalanced data. One of the methods to overcome this data is by using the Synthetic Minority Oversampling Technique (SMOTE) by creating synthetic data on minority data so that the data will be balanced with the majority data. This study aims to see the effect of SMOTE on sentiment analysis using the Support Vector Machine (SVM) algorithm. Based on the evaluation results using two different datasets, it was found that the results of the analysis using the SVM method resulted in an average accuracy of around 79.1% in the covid-19 dataset and 75% in the tv dataset. There was an increase in accuracy when applying the SVM + SMOTE method with an average accuracy of around 93.2% in the covid-19 dataset and 84,7% in the tv dataset.

Availability
Inventory Code Barcode Call Number Location Status
2307004229T93069T930692023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T930692023
Publisher
Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xv, 102 hlm.; Ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
518.107
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Algoritma
Jurusan Teknik Informatika
Specific Detail Info
-
Statement of Responsibility
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No other version available

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