Skripsi

ANALISIS SENTIMEN MENGGUNAKAN PSEUDO NEAREAST NEIGHBOR DAN TF-IDF TEXT VECTORIZER

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Penilaian

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Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000973	T90531	T905312021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T905312021

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

x,76 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.754 07

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
Situs Jejaring Sosial, Sosial Media

Specific Detail Info

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Statement of Responsibility

SRI

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