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Image of KLASIFIKASI EMOSI PADA TEKS TWITTER MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM)

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KLASIFIKASI EMOSI PADA TEKS TWITTER MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM)

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Emotions have an important role in everyday life because the expression of emotions can help to provide information about the status of an individual's interaction with other individuals and their environment. There are six emotions, such as: Happy, sad, scared, disgusted, angry, and surprised. One of the platform that many people choose to express their emo tions is through social media. This study aims to build a software to classify emotions on twitter text using Long Short�Term Memory and determine its performance. Data used in this study were collected through crawling on Twitter using rapidminer to get a total of 24,000 tweets. The data that have been collected then divided into training data and testing data, then the data went through the pre-processing stage before being entered into the LSTM layer. The data were then classified using the LSTM method and trained using 10 fold K-Fold Cross Validation. Based on the results of the classification, it is known that the greatest accuracy is in the K -Fold 5 with the accuracy score of 88.30% and the loss value of 0.022.


Availability
Inventory Code Barcode Call Number Location Status
2207002263T74293T742932022Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T742932022
Publisher
Inderalaya : Jurusan Teknik Informatika, Fakultas Pertanian., 2022
Collation
xvii, 79 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.754 07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Teknik Informatika
Situs Jejaring Sosial
Specific Detail Info
-
Statement of Responsibility
SEPTA
Other version/related

No other version available

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  • KLASIFIKASI EMOSI PADA TEKS TWITTER MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM)
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