Skripsi

ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI IPUSNAS DI GOOGLE PLAYSTORE DENGAN METODE BI-LSTM DAN FASTTEXT

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Penilaian

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Digital libraries, such as iPusnas, have become essential solutions for improving access to reading materials in the digital era. User reviews of the iPusnas application on the Google Play Store contain valuable information that can be utilized to enhance services and guide application development. This study aims to develop a sentiment analysis system for user reviews of the iPusnas application by employing a combination of Bidirectional Long Short-Term Memory (Bi-LSTM) and FastText methods, and to evaluate its performance based on accuracy, precision, recall, and F1-score metrics. Bi-LSTM is an extension of LSTM that processes data in both forward and backward directions, enabling more effective contextual understanding of sentences. Meanwhile, FastText is a word representation technique that incorporates subword information, allowing it to generate more informative word vectors, particularly for languages with complex morphology. The dataset consists of 10,149 reviews classified into three sentiment categories: positive, neutral, and negatif. Optimal results were obtained using a configuration of 64 LSTM Units, 0.2 LSTM dropout, 32 dense Units, a learning rate of 0.001, batch size of 16, and 15 epochs, achieving an accuracy of 79%. The model performed well in classifying positive (F1-score 84%) and negatif (F1-score 83%) sentiments, but showed suboptimal performance in identifying neutral sentiment (F1-score 55%). The developed system can assist iPusnas application developers in better understanding user opinions to improve the quality of digital library services.opinions more effectively to improve the quality of digital library services.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002558	T172525	T1725252025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1725252025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 159 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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