Sentiment Analysis of Bjorka Hacker Using the Naive Bayes and C.45 Algorithms

Wowon Priatna; Eka Nur  A’ini; Joni  Warta; Agus Hidayat; Tyastuti Sri  Lestari; Rasim

doi:10.34306/conferenceseries.v4i1.614

Authors

Wowon Priatna Universitas Bhayangakara Jakarta Raya
Eka Nur A’ini Bhayangkara University of Jakarta
Joni Warta Bhayangkara University of Jakarta
Agus Hidayat Bhayangkara University of Jakarta
Tyastuti Sri Lestari Bhayangkara University of Jakarta
Rasim Bhayangkara University of Jakarta

DOI:

https://doi.org/10.34306/conferenceseries.v4i1.614

Keywords:

BPJS, Hacker Bjorka, Classification, Sentiment Analysis, C.45, Naive Bayes

Abstract

In 2023, Indonesia was again devastated by a hacker known as Bjorka. Bjorka did not act just once or twice; every time, Bjorka made the entire Indonesian population proud. The 19 million BPJS Employment data belonging to the Indonesian people that Bjorka hacked is the BPJS Employment data belonging to the Indonesian people that Bjorka hacked. Since the release of the Bjorka story, there has been a surge in the number of people criticizing it on social media, particularly Facebook, so the criticism or opinions can be used to conduct sentiment analysis. Based on this, developing a method that can automatically classify beliefs into positive and negative categories through sentiment analysis is necessary. The sentiment analysis process begins with data preprocessing, followed by keyword analysis using the TF-IDF method, algorithm development, and analysis of classification results. The data classification methods used in this study are Naive Bayes and C4.5. The data will be analyzed using text mining and classified using the Naive Bayes and C4.5 algorithms. Based on the results of the tests, the best classification was achieved by Nave Bayes, with a score of 70 percent for the C4.5 algorithm and 68 percent for the C4.5 algorithm. The Nave Bayes algorithm can predict up to 70% data transmission rates for both positive and negative signals.

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