Analysis of Sentiment the Issue of Fuel Increase: based on Twitter using the Random Forest Classifier, Naïve Bayes, and Support Vector Machine Methods

MUHAMAD RIKBAL IKHSANI

Informasi Dasar

107 kali
23.04.2727
006.31
Karya Ilmiah - Skripsi (S1) - Reference

Sentiment analysis is a technique used to analyze the subjectivity of opinions expressed in the text. In this research, we evaluate sentiment classification methods for analyzing public opinion about fuel inflation on Twitter, including Naive Bayes, Support Vector Machine (SVM), and Random Forest. Our results show that the SVM and Random Forest methods produced the highest accuracy rates of 78%, while Naive Bayes achieved an accuracy rate of 70%. Based on these findings, the SVM and Random Forest methods are good choices for sentiment analysis on public opinion about fuel price increases on Twitter.

Subjek

Machine - learning
FUEL SYSTEMS,

Katalog

Analysis of Sentiment the Issue of Fuel Increase: based on Twitter using the Random Forest Classifier, Naïve Bayes, and Support Vector Machine Methods
 
 
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

MUHAMAD RIKBAL IKHSANI
Perorangan
Bambang Ari Wahyudi, Irma Palupi
 

Penerbit

Universitas Telkom, S1 Informatika (International Class)
Bandung
2023

Koleksi

Kompetensi

 

Download / Flippingbook

 

Ulasan

Belum ada ulasan yang diberikan
anda harus sign-in untuk memberikan ulasan ke katalog ini