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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JSAI (Journal Scientific and Applied Informatics) INFORMASI (Jurnal Informatika dan Sistem Informasi) JUKI : Jurnal Komputer dan Informatika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknologi Terapan and Sains 4.0 SATIN - Sains dan Teknologi Informasi
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier Fitri Wulandari; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4781

Abstract

The Indonesian government made a policy to stop consuming syrup as a form of prevention against acute kidney failure, which affects many people in Indonesia. However, the policy has caused a lot of comments from the public. These public comments can be found on YouTube, because YouTube has a large data source opportunity to be used as a research material. These comments can be processed directly without using a machine, but it is less effective and efficient. Thus, the comments are processed using machine learning methods. Based on the earlier research, the naive bayes classifier algorithm tends to be simple and easy to use. In addition, this algorithm also has a high accuracy. The amount of data used in this study is 1000 YouTube comment data related to videos regarding the policy of prohibiting the use of syrup medicine, the comments are divided into 2 category, which are positive class and negative class. The results of labeling 1000 comments obtained 704 negative comments and 296 positive comments. Based on the experiments conducted using python programming language, the highest accuracy was obtained at 74% in 70:30 data split. Furthermore, in the balanced dataset (296 positive and 296 negative comments), the highest accuracy was obtained at 64.70% with in 80:20 data split. These results represent that the naive bayes classifier algorithm is good enough at sentiment analysis about the policy of prohibiting the use of syrup drugs.
Co-Authors Alga Prananda Alwis Nazir Ayu Fransiska Damayanti, Elok Detha Yurisna Elin Haerani Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto Fitri Insani Ibnu Surya Inggih Permana Lola Oktavia Muhammad Irsyad Nanda Sepriadi Pizaini Pizaini Pizaini Putri Mardatillah Rahmat Rizki Hidayat Ramadanu Putra Reski Mai Candra Rinaldi Syarfianto Safwandi Safwandi Sayed Omas Tutus Arifta Sayed Sofiah Surya Agustian Suwanto Sanjaya Taufik Hidayat Tiara Dwi Arista Yani, Susmi Syahfrida Yuda Zafitra Fadhlan Yusra YUSRA YUSRA Yusra, Yusra