Klasifikasi Sentimen Opini terhadap Film Kartini Menggunakan Naive Bayes pada Platform X

Film is an audiovisual media that not only serves as entertainment, but can also provide information and touch the emotions of its audience. In the development of the digital era, people tend to use social media to search for films that are popular or trending. One platform that is often used for this purpose is Twitter, which in July 2023 changed its name to ‘X’. This research discusses the sentiment analysis of the film Kartini on the X social media platform, which tells the story of R.A. Kartini's struggle for the rights of Indonesian women. The film became a topic of conversation with various opinions, both positive and negative, circulating on the platform. The amount of public sentiment often creates confusion for potential viewers in determining whether the film is worth watching. These varying views on the film can affect viewers' perceptions, which in turn risks creating a disappointing viewing experience and making viewers feel that the time spent was not worthwhile. Therefore, the researcher aims to classify the sentiment of platform X users towards the film Kartini using the Naive Bayes Classifier method, which works based on Bayes' Theorem to predict the probability of an event based on previous data. This method was chosen due to its ability to efficiently classify sentiment into positive or negative categories. Temporary results on testing data using the naive bayes algorithm and python programming language obtained very good results. The best accuracy result of the emotion dataset obtained is 98% and the best accuracy of the sentiment dataset obtained is 86%.

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Publication Details

Journal
Journal of Computer System and Informatics (JoSYC)
Published
2024-11-29
DOI
https://doi.org/10.47065/josyc.v6i1.6208
Primary Topic
Multimedia Learning Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Klasifikasi Sentimen Opini terhadap Film Kartini Menggunakan Naive Bayes pada Platform X

Joko Aryanto, Muhammad Ramadhani, Iwan Hartadi Tri Untoro, Agus Sujarwadi
Journal of Computer System and Informatics (JoSYC)
Multimedia Learning Systems
article

Klasifikasi Sentimen Opini terhadap Film Kartini Menggunakan Naive Bayes pada Platform X

Joko Aryanto, Muhammad Ramadhani, Iwan Hartadi Tri Untoro, Agus Sujarwadi
article en

Abstract

Film is an audiovisual media that not only serves as entertainment, but can also provide information and touch the emotions of its audience. In the development of the digital era, people tend to use social media to search for films that are popular or trending. One platform that is often used for this purpose is Twitter, which in July 2023 changed its name to ‘X’. This research discusses the sentiment analysis of the film Kartini on the X social media platform, which tells the story of R.A. Kartini's struggle for the rights of Indonesian women. The film became a topic of conversation with various opinions, both positive and negative, circulating on the platform. The amount of public sentiment often creates confusion for potential viewers in determining whether the film is worth watching. These varying views on the film can affect viewers' perceptions, which in turn risks creating a disappointing viewing experience and making viewers feel that the time spent was not worthwhile. Therefore, the researcher aims to classify the sentiment of platform X users towards the film Kartini using the Naive Bayes Classifier method, which works based on Bayes' Theorem to predict the probability of an event based on previous data. This method was chosen due to its ability to efficiently classify sentiment into positive or negative categories. Temporary results on testing data using the naive bayes algorithm and python programming language obtained very good results. The best accuracy result of the emotion dataset obtained is 98% and the best accuracy of the sentiment dataset obtained is 86%.

Journal of Computer System and Informatics (JoSYC)Vol. 6(1)
Universitas Teknologi Yogyakarta (ID)
Openalex Percentile: Top 51%
Multimedia Learning Systems
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