Analisis Sentimen Kenaikan Harga BBM Pertamax Pada Media Sosial Menggunakan Metode Naïve Bayes Classifier

Sartika Lina Mulani Sitio; Ria Nadiyanti

doi:10.47065/bits.v4i3.2311

Sartika Lina Mulani Sitio * Universitas Pamulang, Tangerang Selatan, Indonesia
Ria Nadiyanti Universitas Pamulang, Tangerang Selatan, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v4i3.2311

Keywords: Sentiment Analysis; Naïve Bayes Classifier; Facebook; Pertamax Fuel Price Increase; Classification

Abstract

Fuel Oil (BBM) is a very vital commodity. Fuel has an important role in people's lives. Because of the importance of fuel in people's lives, fuel is one of the basic needs of the community. The policy of increasing the price of fuel has always been a phenomenon in various media which causes pros and cons in society. The policy of increasing fuel prices has a big impact on society, both direct and indirect consumption. This study aims to explore public opinion, whether it shows negative or positive sentiment in the policy of increasing fuel prices. The increase in Pertamax fuel prices has drawn several opinions from citizens on Facebook social media. Sentiment analysis research was conducted to determine the response to Facebook comments on Brilio.Net accounts in 2022 related to the increase in Pertamax fuel prices with a dataset of 799 data, as well as a comparison of the number of positive, negative, and neutral comments. In addition, in this study to be able to determine the level of performance generated by the nave Bayes classifier method in the test. The author uses 80% of the comment dataset to be used as training data and 20% to be used as test data to be used as machine learning and test data. Then the data is classified by the system using orange data mining tools so as to produce a percentage of positive sentiment as much as 19%, negative sentiment as much as 22% and neutral sentiment as much as 59%. testing with the nave Bayes classifier method obtained the highest percentage accuracy rate of 99% from all datasets.

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