Analisis Sentimen Masyarakat terhadap Penggunaan Teknologi AI dengan Metode Machine Learning
DOI:
https://doi.org/10.55606/juisik.v5i2.1198Keywords:
Machine Learning, Naive Bayes, Sentiment, Social Media, SVMAbstract
This study discusses public perceptions of the increasingly widespread use of machine-based technology in everyday life. One approach to understanding this perception is through sentiment analysis conducted on public opinion on social media. Using machine learning methods, this study classifies public sentiment into three categories: positive, negative, and neutral. Data was collected through the Twitter social media stage and processed using the CRISP-DM approach. Three algorithms were used in the classification, namely Bolster Vector Machine (SVM), Credulous Bayes, and Choice Tree. The evaluation results showed that SVM provided the highest accuracy in classifying sentiment data. The majority of public opinion was neutral, but there were concerns regarding social and ethical impacts. This study contributes to a general understanding of public perceptions of machine-based technology that are increasingly dominating various sectors.
References
Anjani, D. M., & Wibowo, H. (2023). Klasifikasi sentimen komentar pengguna media sosial pada isu lingkungan hidup. Jurnal Informatika Nusantara, 8(2), 93–101.
Corrs, A. A., Syam, A., & Aris, V. (2025). Analisis sentimen tren cryptocurrency menggunakan machine learning. Jurnal Teknologi dan Informatika, 3(4).
Furqan, M. (2020). MFQ-2020-1. Jurnal Informatika dan Sistem Informasi, 5(2), 112–118.
Halim, S., & Nugroho, A. (2024). Perbandingan algoritma machine learning untuk klasifikasi sentimen layanan publik. Jurnal Teknologi dan Aplikasi, 10(1), 59–66.
Hsb, D. U., Furqan, M., Islam, U., & Sumatera, N. (2024). Sentiment analysis of Instagram social media users for BPJS Health Services using Support Vector Machine algorithm. Jurnal Sistem Cerdas, 8(158), 68–73.
Jtik, J., Teknologi, J., & Kiedrowsky, F. F. (2023). Sentiment analysis marketplaces digital menggunakan machine learning. Jurnal Teknologi Informasi dan Komunikasi, 7(3).
Maulana, F., & Prasetyo, Y. (2020). Sentiment analysis pada platform Twitter mengenai Pilkada serentak 2020 menggunakan metode K-Nearest Neighbor. Jurnal Informatika dan Komputer, 6(2), 77–84.
Pratama, A. D. (2024). Analisa sentimen masyarakat terhadap penggunaan ChatGPT menggunakan metode Support Vector Machine (SVM). Jurnal Ilmiah Komputer dan Informatika, 9(1), 327–338.
Putra, R. D., & Salim, A. (2022). Analisis sentimen pada komentar YouTube menggunakan metode Support Vector Machine. Jurnal Teknologi Informasi, 14(3), 210–217.
Ramadhani, L., & Fatimah, N. (2022). Analisis sentimen terhadap layanan transportasi daring di Indonesia menggunakan SVM dan lexicon-based. Jurnal Ilmu Komputer, 7(4), 178–185.
Santoso, R., & Lestari, M. (2021). Penerapan algoritma Naïve Bayes untuk klasifikasi sentimen Twitter tentang vaksin Covid-19. Jurnal Media Informatika, 9(2), 145–150.
Syahrohim, I., Saputra, S. D., & Saputra, R. W. (2024). Analisis sentimen PILPRES 2024 di Twitter menggunakan Naïve Bayes dan SVM. Jurnal Teknik Informatika, 12(2).
Widodo, T., & Mulyadi, D. (2023). Pemanfaatan algoritma decision tree untuk analisis sentimen konsumen marketplace. Jurnal Sistem Informasi dan Teknologi, 4(3), 201–208.
Wulandari, S., & Kurniawan, A. (2023). Sentiment analysis terhadap review e-commerce menggunakan metode Naïve Bayes dan KNN. Jurnal Sistem Informasi, 11(1), 54–61.
Zulkarnain, A., & Hasibuan, A. (2022). Implementasi machine learning dalam klasifikasi sentimen film di IMDb. Jurnal Artificial Intelligence Indonesia, 2(1), 31–40.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
