Skripsi

KLASIFIKASI ULASAN PALSU MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

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Penilaian

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Customer reviews play a crucial role in shaping purchasing decisions. The significance of these reviews has led many individuals to create fake reviews purposely for personal gain. Fake reviews can result in unhealthy business competition, damage a business's reputation which leads to financial losses, and diminish consumer trust. This research aims to classify reviews into fake and genuine categories. This research utilizes the Support Vector Machine (SVM) method to classify data due to SVM's advantages in handling datasets with many features. The research integrates features including review helpful, sentiment, subjectivity, word count, noun count, adjective count, verb count, adverb count, authenticity, and analytical thinking. Testing is conducted using data split ratios of 60:40, 70:30, 80:20, and 90:10, with parameter C values set at 0.1, 1, and 10 for each split ratio. The research findings indicate that the SVM model for classifying fake reviews, with a dataset split ratio of 70:30 for training and testing and a C value of 1, demonstrates the best performance in terms of accuracy, precision, and F-Measure, achieving 95.2%, 97.62%, and 95.60%, respectively.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407000232	T137750	T1377502023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1377502023

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 86 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.507

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Program Aplikasi dengan Kegunaan Khusus

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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