Sentiment Analysis on Twitter Social Media Application on Fuel Oil Price Hike Using Naïve Bayes and Decision Tree Algorithms

Muhammad Fadli; Agung Triayudi; Endah Tri Eshti Handayani

doi:10.58905/sana.v2i2.271

Authors

Muhammad Fadli Information System Departement, Faculty of Communications and Information Technology, Universitas Nasional, Jakarta, Indonesia
Agung Triayudi Information System Departement, Faculty of Communications and Information Technology, Universitas Nasional, Jakarta, Indonesia
Endah Tri Eshti Handayani Information System Departement, Faculty of Communications and Information Technology, Universitas Nasional, Jakarta, Indonesia

DOI:

https://doi.org/10.58905/sana.v2i2.271

Keywords:

Decision Tree, Fuel Oil, Naive Bayes Classifier, Price

Abstract

The increase in fuel prices has significant impacts on the Indonesian community's economic sector. Most people object to this policy because of its significant effects on daily life. Additionally, Indonesia's economy has not fully recovered from the Covid-19 pandemic, compounded by news of rising oil prices. With news of the increase in fuel prices, most people express sentiment regarding the rise in fuel prices on one of the social media platforms, Twitter. This study aims to differentiate the sentiment provided by the public, whether positive, negative, or neutral, using the Naïve Bayes Classifier and Decision Tree algorithms. The analysis results show that the Naïve Bayes Algorithm model, specifically Bernoulli Naïve Bayes, achieves the highest accuracy of 65.60%, with a precision of 68%, recall of 60.30%, and f1-score of 59.33%.

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