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All Journal Jupiter Jurnal sistem informasi, Teknologi informasi dan komputer Sistemasi: Jurnal Sistem Informasi DoubleClick : Journal of Computer and Information Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information Systems and Informatics Informanika Journal of Computer and Information Systems Ampera Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian kepada Masyarakat Bina Darma JSK (Jurnal Sistem Informasi dan Komputerisasi Akuntansi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Nurul Adha Oktarini Saputri
Universitas Bina Darma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : Journal of Computer and Information Systems Ampera

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Analisis Sentimen Masyarakat Terhadap Pilpres 2019 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier Khoirul Zuhri; Nurul Adha Oktarini Saputri
Journal of Computer and Information Systems Ampera Vol. 1 No. 3 (2020): Journal of Computer and Information Systems Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalcisa.v1i3.45

Abstract

Twitter is a social media that is currently popular, where the public is free to comment and write anything. It is not uncommon for the public to comment with harsh words and even hate speech. The 2019 presidential election drew many comments, some praised, criticized and insulted. To be able to dig up information and classify a text, sentiment analysis is needed. In this study, sentiment analysis is a process of classifying textual documents into two classes, namely negative and positive sentiment classes. Opinion data were obtained from the Twitter social network in the form of tweets. The data used was 3337 tweets consisting of 80% training data and 20% training data. Training data is data with known sentiment. This study aims to determine whether a tweet is a positive or negative tweet conveyed on Twitter in Indonesian. The classification of tweet data uses the naïve Bayes classifier algorithm. The classification results of the test data show that the Naïve Bayes Classifier algorithm provides an accuracy value of 71%. The accuracy value for each sentiment is 71% for positive sentiment and 70% for negative sentiment
Co-Authors Alvin Alvin Desi Ratna Putri DINNY KOMALASARI Elma Amorita Firamon Syakti Hadi Syaputra Jodi Astama Khoirul Zuhri M. Soekarno Putra Merriayu Puspita Hannah Merrieayu Puspita Hannah Misinem Misinem Monica Elvirasari Muhamad Akbar Oktaviani, Nia Prihambodo Hendro Saksono Rabi'ah Rian Anggara Ryan Andri Septian Dwi Fernando Susan Dian Purnamasari Trie Martha Wahyu Rahmana Putra