Skripsi

ANALISIS SENTIMEN ULASAN PRODUK MENGGUNAKAN BIDIRECTIONAL LONG SHORT-TERM MEMORY DAN WORD EMBEDDING

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Penilaian

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Sentiment analysis is a computational study of human opinions, sentiments, emotions, and behavior toward entities or attributes expressed through written text. Sentiment analysis plays a significant role for companies and organizations because public opinion about their products and services is valuable for business strategy and evaluation. This research developed a system to classify product review sentiment using Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm and Word Embedding, Word2Vec as the embedding layer. The built system uses two models, the base model, which has the same parameter configuration as the CNN model used in previous research, and the tuned model, whose parameter configuration is based on the results of hyperparameter tuning. The results showed that the second model has the best performance with an accuracy of 90.33%, precision of 99.41%, recall of 90.29%, and F1-Score of 94.61%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003430	T105630	T1056302023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1056302023

Publisher

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

Collation

xv, 103 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)

Jurusan Teknik Informatika
Memory

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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