Skripsi

ANALISIS SENTIMEN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN QUERY EXPANSION

---

Penilaian

---

Sentiment analysis is used to determine someone's opinion on a topic, then classify the comments into positive or negative sentiments. Support Vector Machine (SVM) is one of thealgorithms supervised learning that predicts classes based on models or patterns from the results of theprocess training. Themethod Support Vector Machine (SVM) in general still has shortcomings when applied toshort text, one of the challenges of using short text as a case of text classification is ambiguity. One technique that can be used to help with this problem is to add afeature or query newcalled query expansion(QE). In this study, the data used are comments about LRT SUMSEL on Twitter. The test results show that the application of query expansion with the value of window size 2 has an accuracy of 76% while the value of window size 3 is 74%. Sentiment analysis using support vector machine without query expansion has an accuracy of 83%

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002180	T54257	T542572021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T542572021

Publisher

Inderalaya : Fakultas Ilmu Komputer, Prodi Teknik Informatika., 2021

Collation

xvi, 65 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Teknik Informatika
Pemrosesan Data, Ilmu Komputer, Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment