Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier

Authors

  • Muhammad Muslimin Universitas Stikubank, Semarang
  • Veronica Lusiana Universitas Stikubank, Semarang

DOI:

https://doi.org/10.30865/mib.v7i3.6418

Keywords:

Community, Sentiment Analysis, Twitter, Basic Materials, Naïve Bayes Classifier

Abstract

Basic necessities are the main needs that are important for people’s lives. The increase in the price of basic necessities certainly has a huge impact on the operational costs of the community and has become a very crucial issue. This event gave rise to pro and con responses from the public expressed through social media Twitter. From this event, sentiment analysis research was conducted related to the increase in the price of basic commodities. The amount of data used for this research is 2070 tweet data. The analysis results show that negative sentiment appears more than positive sentiment, with a percentage of 2,8% positive sentiment and 97,2% for negative sentiment. Retrival of tweet data is done through the netlytic.org website with the keyword staples. The classification method uses the naïve Bayes Classifier method. Furthermore, data division is carried out on the dataset with a ratio of 6:4. The data is divided into 60% training data and 40% test data. The size of the test data as much as 40% of the overall data produces the best accuracy rate. From the results of testing the model with the Naïve Bayes Classifier method the evaluation value results are the highest accuracy score of 94,38%, precision of 59,67%, recall of 67,93%, and F-measure of 62,32%. It can be concluded that the results of sentiment analysis on the increase in the proce of basic commodities get a negative response from the public. This research proposes a method of sentiment analysis of rising prices of basic commodities by considering the level opinion sentiment on Twitter.

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Published

2023-07-23

Issue

Vol. 7 No. 3 (2023): Juli 2023

Section

Articles

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