Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Juliar Husriansyah
Universitas Malikussaleh
Computer Science & IT Engineering

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

Found 1 Documents
Search

 1 
PENERAPAN DECISION TREE C5.0 DALAM APLIKASI ANALISIS SENTIMEN TERHADAP BOIKOT PRODUK PRO-ISRAEL DI MEDIA SOSIAL X: SENTIMENT CLASSIFICATION ON BOYCOTT-RELATED TWEETS USING C5.0 DECISION TREE ALGORITHM Juliar Husriansyah; Asrianda Asrianda; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6451

Abstract

The movement to boycott products believed to support Israel reflects global solidarity with the Palestinian fight. In Indonesia, support for this movement continues to grow, especially through social media platform X (formerly Twitter). After the release of MUI Fatwa Number 83 of 2023, which advises Muslims to refrain from using products linked to Israel. The objective of this study is to analyze the sentiment of users on social media X regarding the boycott of products that support Israel, using the Decision Tree C5.0 algorithm. The data were collected through a scraping technique targeting tweets containing relevant boycott-related keywords, then processed using text preprocessing and analyzed using Term Frequency-Inverse Document Frequency (TF-IDF) for the extraction of features. The dataset was divided into 80% for training and 20% for testing in order to train and assess the classification model. The classification results revealed that out of 1,840 tweets, 1,257 were positive, 318 negative, and 265 neutral, indicating that 68.32% of users expressed support for the boycott movement. The evaluation of the model resulted in an accuracy of 83.26%, a precision of 86.51%, a recall of 83.26%, and an f1-score of 84.29%, demonstrating that the C5.0 algorithm effectively and accurately classifies sentiment. This research is anticipated to act as a guide for creating systems that analyze public opinion and provide insights for policymakers and industry players in responding to social issues emerging on digital platforms.
Co-Authors Asrillah Asrillah Said Fadlan Anshari