Skripsi

ANALISIS SENTIMEN PERSEPSI PENGGUNA TWITTER MENGENAI PENERAPAN PENDETEKSI WAJAH DENGAN PENDEKATAN LONG SHORT TERM MEMORY (LSTM)

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Penilaian

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In this study, an analysis of Twitter users' opinions and perceptions regarding the implementation of facial recognition will be conducted, with a focus on performance and privacy security. The method to be applied is Long Short Term Memory (LSTM) and Word2Vec weighting. LSTM, also known as deep learning, is chosen because it can process and retain long-term information, making it suitable for analyzing complex and contextual texts such as Twitter posts. The results of experimental testing show a good level of accuracy, with values of accuracy 0.7739, precision 0.7738, recall 0.7739, and F1-Score 0.7737. The use of hyperparameters such as dropout 0.3, hidden unit 64, recurrent dropout on LSTM layer 0.8, Epochs 30, and batch size 128 contribute positively to the model's performance. This research provides an in-depth understanding of users' attitudes towards facial recognition technology, especially in the context of performance and privacy security, and offers insights into the development of more responsive and high-quality technology.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004074	T149929	T1499292024	Central Library (REFERENCES)	Available

Detail Information

Series Title

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Call Number

T1499292024

Publisher

Indraya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xviii, xviii hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.754 07

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Analisis sentimen
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

UIN YOLA

Other version/related

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