Skripsi

ANALISIS SENTIMEN PADA ULASAN PENGGUNA APLIKASI AJAIB DENGAN ALGORITMA LONG SHORT-TERM MEMORY

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Penilaian

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Ajaib is one of the largest applications in Indonesia that focuses on stock and mutual fund investments. User reviews of the application are one way for Ajaib to understand user needs and make improvements to the application. This research aims to analyze the sentiment in user reviews of the Ajaib application. Long Short-Term Memory (LSTM) is used as the method for sentiment analysis, and Word2Vec is used for feature extraction. The data used in this research consists of 1,111 user reviews of the Ajaib application from the Google Play Store. The testing phase includes 6 experimental scenarios, and the best results were obtained with the following hyperparameters: dropout layer value is 0.2, 32 LSTM neurons, learning rate value is 10-3, LSTM dropout value is 0.5, batch size value is 8, and 30 epochs. The results show the highest accuracy and f-measure values is 89.69% and 89.72%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002537	T114317	T1143172023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1143172023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xviii, 89 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

penyimpanan
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

FIRA

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