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All Journal Infotech Journal Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research SMARTICS Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik BERNAS: Jurnal Pengabdian Kepada Masyarakat IJISTECH Industrial Research Workshop and National Seminar J-ENSITEC (Journal of Engineering and Sustainable Technology) West Science Interdisciplinary Studies West Science Interdisciplinary Studies
Ade Bastian
Universitas Majalengka
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Sentiment Analysis Against Political Figure’s Billboard During Pandemic Using Naïve Bayes Algorithm Ade Bastian; Ardi Mardiana; Dinda Sri Wulansari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4643

Abstract

In the midst of the Covid-19 Pandemic, many Indonesians have reacted negatively to the placement of political individuals' billboards with very huge sizes on the streets. The early political campaign that was run was thought to be contentious. On social media like Twitter, the majority of people freely share their thoughts. The purpose of this study is to investigate how the general public reacted to the placement of billboards advertising political figures during the epidemic and to categorize those responses. It is envisaged that it would also provide advice for connected parties that may be used when making judgments regarding the policy of constructing billboards for political figures during a pandemic based on the results of data analysis. Twitter users tend to be more expressive because of the character limits, which means they have sentimental or emotional values. Using the Nave Bayes Algorithm, it is possible to do sentiment analysis on the sentiment data by categorizing user comments into positive, negative, and neutral attitudes. Regarding the sentiments expressed on billboards showing political leaders during the pandemic, tweets were sorted into three categories: liked, unfavorable, and neutral. The accuracy rate from Naive Bayes categorization of political personalities during the pandemic on social media Twitter was 83.3% with a precision value of 89%, recall 83%, and f-1 score of 82%.
Co-Authors Ardi Mardiana Ardi Mardiana Arif Yusuf Budiman Budiman Dadan Romadhoni Dadan Zaliluddin Devi Sukrisna Diana Surya Heriyana Didin Rudini Dinda Sri Wulansari Dony Susandi Dony Susandi Erdiyanti, Yucky Putri Harti, Adi Oksifa Rahma Harun Sujadi Ii Sopiandi, Ii Latiful Abror Lidya Tresna Wahyuni Muhammad Iqbal Rizmaya Muhammad Syifa Al Maroghi Nadya Pratiwi Aisha Bakhtiar Rezha Nugraha Riki Riyanto Sandi Fajar Rodiansyah Tika Sifana Tri Ferga Prasetyo Usup Suparma Wahyuni, Lidya Tresna Whydiantoro Yofi Awwaluddin