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Abstract

Cooking oil is a basic need for Indonesian people. Indonesia experienced a shortage of oil in March 2022. This was a hot topic of discussion on social media Twitter last March, many people thought positively or negatively. However behind it all, there are differences in the assessment of parties who feel the pros and cons, various parties have different points of view. In this article, we conduct a sentiment analysis of public responses regarding the scarcity of cooking oil using a dataset obtained from the Twitter digital platform. This article aims to classify tweets related to the scarcity of cooking oil into positive and negative sentiments using a machine learning strategy with the Naive  Bayes and lexicon based methods. This algorithm was chosen to make it easier for interested users to compare methods and find out how accurate it is, which is where the level of accuracy obtained from the lexicon method is 42% and the method using the naïve Bayes classifier is 72%. Shows the results of the analysis related to the scarcity of cooking oil for the highest level of accuracy, namely the method that uses the naïve Bayes classifier compared to the method that uses lexicon based

Keywords

minyak goreng naive bayes classifier lexicon based cooking oil, naïve bayes classifier, lexicon based

Article Details

Author Biographies

Faldy Irwiensyah, Universitas Muhammadiyah Prof. Dr. Hamka

Program Studi Teknik Informatika

Firman Noor Hasan, Universitas Muhammadiyah Prof. Dr. Hamka

Universitas Muhammadiyah Prof. Dr. Hamka

How to Cite
Irwiensyah, F., & Hasan, F. N. (2023). Perbandingan Akurasi Metode Naïve Bayes Classifier dan Lexicon Based pada Analisis Sentimen Respon Masyarakat Tentang Kebijakan Kenaikan Harga Minyak Goreng. Jurnal Teknik Informatika Dan Komputer, 2(1), 18–23. https://doi.org/10.22236/jutikom.v2i1.11500
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