Classification of Youtube User sentiment on 5G Technology Videos with Naïve Bayes

Taupik Hidayat; Agus Bahtiar; Kaslani

doi:10.59934/jaiea.v4i3.992

Authors

Taupik Hidayat STMIK IKMI Cirebon
Agus Bahtiar STMIK IKMI Cirebon
Kaslani STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.992

Keywords:

Analytics Sentiment, Naïve Bayes, 5G Technology, YouTube, TF-IDF

Abstract

The rapid development of 5G technology has triggered various reactions from the public on social media platforms such as YouTube. User sentiment towards videos discussing 5G technology varies, from positive to negative. This research aims to improve the sentiment classification model of YouTube user reviews of videos about 5G technology with the Naïve Bayes algorithm, which is known to be able to handle large text data and sentiment variations. This research goes through the stages of collecting review data from YouTube, data preprocessing including tokenization, stop word removal, and stemming, and sentiment classification into positive, neutral, and negative categories using Naïve Bayes. The bag-of-words (BOW) technique is used to improve the algorithm's performance. The results showed a sentiment distribution of 1,581 neutral, 1,165 positive, and 517 negative. The proposed model achieved 98% accuracy, with macro average precision 0.99, recall 0.98, and F1-score 0.98. Weighted average resulted in precision 0.98, recall 0.98, and F1-score 0.98. These results show the model performs very well in sentiment classification. This research is expected to make a significant contribution in understanding public perception of 5G technology.

Downloads

Download data is not yet available.

References

H. U. Mustakim, “Tantangan Implementasi 5G di Indonesia,” INTEGER J. Inf. Technol., vol. 4, no. 2, pp. 1–10, 2019, doi: 10.31284/j.integer.2019.v4i2.561.

E. F. Saputra and M. R. Pribadi, “Analisis Sentimen Komentar Pada Kanal Youtube The Lazy Monday Menggunakan Algoritma Naive Bayes,” MDP Student Conf., vol. 2, no. 1, pp. 17–23, 2023, doi: 10.35957/mdp-sc.v2i1.4283.

S. Mujahidin, B. Prasetio, and M. C. C. Utomo, “Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes,” Voteteknika (Vocational Tek. Elektron. dan Inform., vol. 10, no. 3, p. 17, 2022, doi: 10.24036/voteteknika.v10i3.118299.

F. A. Ramadhan, S. H. Sitorus, and T. Rismawan, “Penerapan Metode Multinomial Naïve Bayes untuk Klasifikasi Judul Berita Clickbait dengan Term Frequency - Inverse Document Frequency,” J. Sist. dan Teknol. Inf., vol. 11, no. 1, p. 70, 2023, doi: 10.26418/justin.v11i1.57452.

D. Panca Wilie, “Analisis Sentimen Opini Publik Terhadap Chatgpt di Twitter Menggunakan Metode Naive Bayes,” J. Nas. Ilmu Komput., vol. 4, no. 4, pp. 2746–1343, 2023, [Online]. Available: https://www.kaggle.com/datasets/charunisa/ChatGPT-sentiment-analysis.

R. Rachman and R. N. Handayani, “Klasifikasi Algoritma Naive Bayes Dalam Memprediksi Tingkat Kelancaran Pembayaran Sewa Teras UMKM,” J. Inform., vol. 8, no. 2, pp. 111–122, 2021, doi: 10.31294/ji.v8i2.10494.

Fujiyama, “Pengertian Data Mining dan Implementasinya di Berbagai Sektor,” surabaya telkom university. Accessed: Apr. 16, 2025. [Online]. Available: https://surabaya.telkomuniversity.ac.id/pengertian-data-mining-dan-implementasinya-di-berbagai-sektor/?utm_source=chatgpt.com

S. M. Siroj, I. Arwani, and D. E. Ratnawati, “Analisis Sentimen Opini Publik pada Twitter terhadap Efek Pembelajaran Daring di Universitas Brawijaya menggunakan Metode K-Nearest Neighbor,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 7, pp. 3131–3140, 2021, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/9494

Rahel Lina Simanjuntak, Theresia Romauli Siagian, Vina Anggriani, and Arnita Arnita, “Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Shopee Dengan Menggunakan Algoritma Naïve Bayes,” J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 3, no. 3, pp. 23–39, 2023, doi: 10.55606/teknik.v3i3.2411.

F. Syofiani, S. Alam, and M. I. Sulistyo S, “Analisis Sentimen Penilaian Masyarakat Terhadap Childfree Berdasarkan Komentar di Youtube Menggunakan Algoritma Naïve Bayes,” J. Teknol. Inform. dan Komput., vol. 9, no. 2, pp. 688–703, 2023, doi: 10.37012/jtik.v9i2.1661.

M. Yusuf Rismanda Gaja, I. Maulana, and O. Komarudin, “Analisis Sentimen Opini Pengguna Aplikasi Vidio Pada Ulasan Playstore Menggunakan Algoritma Naive Bayes,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 4, pp. 2767–2774, 2024, doi: 10.36040/jati.v7i4.7197.