Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Teika
Axcel Weku
Universitas Klabat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

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

Found 1 Documents
Search

 1 
Perbandingan Algoritma LSTM Untuk Analisis Sentimen Pengguna Twitter Terhadap Layanan Rumah Sakit Saat Pandemi Covid-19 Anggreiny Rolangon; Axcel Weku; Green Arther Sandag
TeIKa Vol 13 No 01 (2023): TeIKa: April 2023
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/teika.v13i01.3063

Abstract

Sentiment analysis has become a crucial aspect in understanding people’s opinions and emotions on various issues. In this study, we conducted sentiment analysis on tweets related to hospital services during the COVID-19 pandemic using LSTM, BiLSTM, GRU, and SimpleRNN models. The data collection process was carried out using the Twitter API and resulted in 15,093 tweets. The data preprocessing process includes data cleaning, case folding, tokenization, filtering, and stemming. The dataset was divided into 80% for training and 20% for testing. The results showed that the BiLSTM model had the highest accuracy of 86%, followed by the GRU model with an accuracy of 86%, the LSTM model with an accuracy of 85%, and the SimpleRNN model with an accuracy of 75%. The BiLSTM model also had the highest MCC of 71%. The study concludes that the BiLSTM model outperformed other models in predicting the sentiment of tweets related to hospital services during the COVID-19 pandemic. This study’s findings may have significant implications for healthcare providers in enhancing their services’ quality and improving patients’ satisfaction during pandemics.
Co-Authors Anggreiny Rolangon Green Arther Sandag