Analisis Sentimen Komentar Video Putusan MA Terkait Kaesang Menggunakan Metode Naive Bayes
DOI:
https://doi.org/10.61132/saturnus.v2i3.217Keywords:
Sentiment analysis, Naive Bayes, Political dynasty, Supreme Court, Kaesang PangarepAbstract
Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos taken from social media platforms. These comments are then categorized into positive, negative, and neutral sentiments. The results of the study show the distribution of public sentiment towards this issue, providing an overview of how the public responds to the decision. The Naive Bayes method is chosen for its simplicity and its ability to provide reasonably accurate results in text analysis.
References
Agusta, L. (2009). Perbandingan Algoritma Stemming Porter dengan Algoritma Nazief & Adriani untuk Stemming Dokumen Teks Bahasa Indonesia. Konferensi Nasional Sistem dan Informatika.
Dabas, P. K., & Nurdin, G. C. (2021). Analisis Komentar di Video Youtube menggunakan Hadoop. Jurnal Nasional Komputasi dan Teknologi Informasi.
Harpizon, R. K. I., Rasyid, E. B. F. S. H. A., & Rasyid, S. (2022). Analisis Sentimen Komentar di YouTube Tentang Ceramah Ustadz Abdul Somad Menggunakan Algoritma Naïve Bayes. Jurnal Nasional Komputasi dan Teknologi Informasi.
Muthia, D. A. (2017). Analisis Sentimen pada Review Restoran dengan Teks Bahasa Indonesia Menggunakan Naive Bayes. Jurnal Ilmu Pengetahuan dan Teknologi Komputer.
Rahman, W. D. A. (2017). Online News Classification Using Multinomial Naive Bayes. Jurnal Teknologi dan Informasi.
Septian, T. F. A. N. F. I. K. N. J. A. (2019). Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor. Journal of Intelligent Systems and Computation.
Styawati, N. H. A. R. I. A. Y. R. (2021). Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja pada Twitter Dengan Metode Support Vector Machine. Jurnal TRANSFORMATIKA.
Surojudin, A. S. d. N. (2020). Analisis dan Perbandingan Stemming Algoritma Porter dengan Algoritma Ahmad Yusoff Sembok dalam Dokumen Teks Bahasa Indonesia. Seminar Nasional Teknologi Informasi dan Komunikasi STI&K.
Syahril Dwi Prasetyo, F. N., & Shofa Shofiah Hilabi, S. (2023). Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN. Jurnal KomtekInfo.
Ulil Albab, Y. K. P. M. N. F. M. (2023). Optimization of the Stemming Technique on Text Preprocessing President. Jurnal TRANSFORMATIKA.
Zhafira, B. R., & Irwanda, I. D. F. (2021). Analisis Sentimen Kebijakan Kampus Merdeka Menggunakan Naive Bayes dan Pembobotan TF-IDF Berdasarkan Komentar pada Youtube. Jurnal Teknologi Informasi dan Ilmu Komputer (JUST-SI).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Saturnus : Jurnal Teknologi dan Sistem Informasi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.