COMPARISON OF NAÏVE BAYES ALGORITHM AND SUPPORT VECTOR MACHINE IN SENTIMENT ANALYSIS OF BOYCOTT ISRAELI PRODUCTS ON TWITTER

Laisha Amilna Hayurian; Nirwana Hendrastuty

doi:10.52436/1.jutif.2024.5.3.1813

Laisha Amilna Hayurian Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia
Nirwana Hendrastuty Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1813

Keywords: Boycott of Israeli Products, Naïve Bayes, Support Vector Machine, Public Sentiment, Twitter, Text Mining

Abstract

The Israeli-Palestinian conflict has captured the attention of Indonesians and even the world for decades, with the death toll reaching 17,000 Palestinians. Indonesians have expressed various opinions, including a proposed boycott of products that allegedly support Israel as a form of protest against the ongoing conflict. This study explores the opinions and sentiments of the Indonesian people regarding the Israel-Palestine conflict and the efforts to boycott Israeli products on social media twitter. This study aims to compare the accuracy of the two algorithms in classifying sentiment towards boycotting Israeli products. A total of 2288 comment data were processed using the Naïve Bayes and Support Vector Machine (SVM) algorithm classification methods. The results show that the Naïve Bayes algorithm has higher accuracy with a data division ratio of 70:30 and 80:30 for training data and testing data. Accuracy results with 70:30 data division reached 84% using the Naïve Bayes algorithm model, while the SVM algorithm model reached 78%. And the accuracy results with 80:20 data division reached 85% using the Naïve Bayes algorithm model, with the SVM algorithm model reaching 84%. This study provides an understanding of the concept of text mining and data mining and can be a reference for similar research.

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