Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Informatika
Pirsingki, Nisa
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods Pirsingki, Nisa; Wandri, Rizky
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.24537

Abstract

Social media facilitates user communication, both in positive, negative and neutral aspects. One of the popular platforms today is Tiktok, where users can create short videos and interact through comments or private messages, as well as follow the latest news, including the major conflict between Palestine and Israel that has been going on since 1948. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. This study aims to analyze public sentiment towards the news of Palestinian refugees heading to Rafah, using two classification methods: Naive Bayes and Decision Tree. Before classification, the data goes through a preprocessing process and TF-IDF weighting, and the two methods are compared to determine the best accuracy. The results showed that the Naive Bayes Multinomial method with the application of SMOTE produced an accuracy of 85.43%, a precision of 86.22%, a recall of 85.43%, and an f1-score of 85.53%. Meanwhile, the Decision Tree C4.5 method with the application of SMOTE produced an accuracy of 94.23%, a precision of 94.58%, a recall of 94.23%, and an f1-score of 94.22%. Based on the evaluation results, the best method for sentiment analysis of the hashtag #SaveRafah is Decision Tree C4.5.
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods Pirsingki, Nisa; Wandri, Rizky
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12256

Abstract

Social media facilitates user communication, both in positive, negative and neutral aspects. Tiktok is a popular platform that allows users to stay up to date on the latest news, including the major conflict between Palestine and Israel. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. The objective of this study is to evaluate public sentiment regarding the news of Palestinian refugees en route to Rafah. To achieve this purpose, we will examine 2982 comments on TikTok relating to the hashtag #SaveRafah, which will be the data to be trained. Prior to classification, the data will undergo a preprocessing process and TF-IDF weighting. The two classification methods will be compared to ascertain the most accurate approach. Because the data at the labeling stage has a larger percentage of positive data 90.7%, this study will employ the technique SMOTE to address class imbalance in the data set. The results showed that the Naive Bayes Multinomial method with the application of SMOTE produced an accuracy of 85.43%, a precision of 86.22%, a recall of 85.43%, and an f1-score of 85.53%. Meanwhile, the Decision Tree C4.5 method with the application of SMOTE produced an accuracy of 94.23%, a precision of 94.58%, a recall of 94.23%, and an f1-score of 94.22%. Based on the evaluation results, the best method for sentiment analysis of the hashtag #SaveRafah is Decision Tree C4.5.
Co-Authors Rizky Wandri