Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Sherly Ekaputri
STMIK Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Naïve Bayes Classifier untuk Analisis Sentimen Isu Radikalisme Tri A Sundara; Sherly Ekaputri; Sotar Sotar
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.069 KB)

Abstract

The presence of twitter as medium that has been widely used by various groups of people. The community’s habit of posting tweets to assess a government policy discourse can be assessed as one of the media in representing the community’s response to the policy discourse. The policy discourse related to the prohibition of veils and Islamic trousers for the State Civil Apparatus is reaping contradictions from the public, especially twitters users. Public opinion can be analyzes using sentiment analysis. Sentiment analysis is a field of study that analyzes a person’s opinions, sentiments, evaluations, attitudes and emotions expressed in text form. Public opinion in the form of tweets will be collected by web crawling using the twitter application program interface (API). The data is processed using natural language processing (NLP) and python programming language. This study aims to produce information on public sentiment related to the prohibition of wearing veils and Islamic trousers for the State Civil Apparatus. This study uses the Naïve Bayes Classifier algorithm to classify 219 positive tweets and 333 negative tweets. The results of testing the accuracy of this study were 86%.
Co-Authors Sotar Tri A Sundara