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Brama Arya Di Putra, I Putu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics Other

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PENERAPAN ALGORITMA NAÏVE BAYES CLASSIFICATION UNTUK KLASIFIKASI SENTIMENT TWEET TERHADAP PLATFORM STREAMING ILEGAL Wahyu, Angga Sukma; Wibowo, Jati Sasongko; Brama Arya Di Putra, I Putu
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.433

Abstract

According to a survey by the Coalition Against Piracy (CAP) of the Asia Video Industry Association, 63% of onlinestreaming service users in Indonesia prefer to watch illegal streaming platforms for free with various "consequences." This makes Indonesia become one of the nations with the highest percentage of users of illegal streaming platforms.In the other side, illegally stream can also violate broadcasting rights rules, which can result some of comments from internet users, including those with both positive and negative feeling about the existence of this illegal streaming platforms.Comments in the form of these sentiments may be found in a variety of media, one of which is the social media site Twitter. Twitter serves as a platform for the expression of thoughts and sentiments as well as for enjoy free time. This research was conducted to find out the comments and opinions of netizens about illegal streaming platforms and will be classified into positive and negative sentiments using the Naïve Bayes Classification Algorithm with the help of the TF-IDF (Term Frequency – Inverse Document Frequency) weighting method. In this study, balanced results were obtained where positive sentiment data was 50% while negative sentiment data was 50% with the value of accuracy 50%, precission 53%, recall 51%, and f-measure 51.
Co-Authors Jati Sasongko Wibowo Wahyu, Angga Sukma