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All Journal Sainteks Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) EDUMATIC: Jurnal Pendidikan Informatika Zonasi: Jurnal Sistem Informasi JIKA (Jurnal Informatika) Idealis : Indonesia Journal Information System Jurnal Pendidikan dan Teknologi Indonesia INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS J-Icon : Jurnal Komputer dan Informatika Teknomatika: Jurnal Informatika dan Komputer
Erik Iman Heri Ujianto
Universitas Teknologi Yogyakarta
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Analisis Sentimen Publik di Twitter Pasca Debat Kelima Pilpres 2024 dengan Naive Bayes Zharifa, Anjana Haya Atha; Ujianto, Erik Iman Heri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28048

Abstract

The presidential election in Indonesia is a frequently discussed topic on social media, especially Twitter. This platform provides a space for people to express their views on presidential candidates and election issues, making it suitable as a data source for this study. This study aims to analyze public sentiment towards presidential election news on Twitter using the Naïve Bayes Classifier method. Data was taken from Twitter for the period 5–13 February 2024 with a total of 2,561 comments. The research process includes data collection, preprocessing, data labeling, and model training and testing. Naïve Bayes was chosen because it is efficient in text classification and has several variants for model experiments. Sentiment is classified into three main categories, namely positive, negative, and neutral. The results showed that negative comments dominated (41%), followed by positive (37.3%) and neutral (21.7%). The Multi Naïve Bayes Classifier model provided the highest accuracy (81%), followed by Bernoulli Naïve Bayes (80%) and Gaussian Naïve Bayes (76%). This difference in accuracy is influenced by the model's sensitivity to data characteristics, such as the number of features and sentiment distribution. This research has the potential to help campaign teams understand the issues that trigger negative responses and support policy makers in designing more effective political communication strategies.
Co-Authors Adhi Prahara ADITYA HERMAWAN Agus Rakhmadi Mido Amelia Citra Dewi Apriansyah, Ferryma Arba Dian Wijayanti Enggar Novianto Fauzi, Rizky Gunawan Wibisono Iman, Asri Khayatul Mido, Agus Rakhmadi Muhammad Husni Wahid Muhammad Rizki Murinto Murinto Purwanto, Candra Ihsan Rianto Rianto Rianto Santoso, Firdaus Restu Rafi Saputra, Candra Heru Syahab, Alfin Syarifuddin Tri Waluyo Triyas Hevianto Saputro Zharifa, Anjana Haya Atha