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PENERAPAN METODE LONG-SHORT TERM MEMORY (LSTM) DALAM ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN APLIKASI GOJEK MELALUI PLAY STORE

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This study discusses the application of the Long Short-Term Memory (LSTM) method in aspect-based sentiment analysis of reviews on the Gojek application in the Google Play Store. By utilizing Word2Vec for word representation and Latent Dirichlet Allocation (LDA) for topic modeling, this research aims to identify and classify user sentiment regarding various features of the Gojek app, such as user experience, service, pricing, and privacy & safety. The dataset consists of Indonesian-language reviews that have undergone preprocessing steps such as case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Topic modeling is performed to determine the main aspects in the reviews, which are then used for sentiment analysis with the LSTM model. The experiment is conducted using the best hyperparameter configuration, including 128 LSTM units, dropout 0.2, recurrent dropout 0.3, learning rate 0.001, batch size 128, and 10 epochs.The evaluation results show that the LSTM model performs well, achieving 91% accuracy, 97% precision, 91% recall, and 94% F1-score. This approach is expected to contribute to improving the quality of Gojek's services

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2507001744T169465T1694652025Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1694652025
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xxiii, 37 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
005.307
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Teknik komputer
Prodi Teknik Informatika
Specific Detail Info
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Statement of Responsibility
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