Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal KLIK: Kajian Ilmiah Informatika dan Komputer
Acep Setiawan
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Sentiment Analisis Opini Masyarakat Sistem Ganjil Genap di Twitter Menggunakan Algoritma Naive Bayes Classifier dan Algoritma K-NN Acep Setiawan; Arafat Febriandirza
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.1837

Abstract

This journal's abstract addresses sentiment analysis of public opinion in relation to the odd-even system's implementation on Twitter, utilizing the K-NN and Naïve Bayes Classifier algorithms. The odd-even system was discussed in tweets by Twitter users, which served as the source data. The tweets were categorized into three sentiment categories: positive, negative, and neutral. The analysis's findings indicate that, of the total number of tweets gathered, 391 were categorized as neutral, 50 as negative, and 59 as positive. In addition, it was found that the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm both had an average accuracy rate of approximately 79.72%. This suggests that both algorithms do similarly well when it comes to classifying the sentiment of the tweets under discussion. With respect to sentiment analysis of public opinion on the Twitter platform, this conclusion clarifies the performance comparison between the Naïve Bayes and K-Nearest Neighbor algorithms.
Co-Authors Febriandirza, Arafat