SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO

  • Lestari Yusuf (1) STMIK Nusa Mandiri Jakarta https://orcid.org/0000-0003-3081-389X
  • Siti Masripah (2*) Universitas Bina Sarana Informatika

  • (*) Corresponding Author
Keywords: chatGPT, Sentiment-analysis, naive bayes

Abstract

Abstract ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages ​​without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..

Downloads

Download data is not yet available.

References

Astari, N. M. A. J., Dewa Gede Hendra Divayana, & Gede Indrawan. (2020). Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier. Jurnal Sistem Dan Informatika (JSI), 15(1), 27–29. https://doi.org/10.30864/jsi.v15i1.332

Dowling, M., & Lucey, B. (2023). ChatGPT for (Finance) research: The Bananarama Conjecture. Finance Research Letters, 53(January), 103662. https://doi.org/10.1016/j.frl.2023.103662

Duei Putri, D., Nama, G. F., & Sulistiono, W. E. (2022). Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Informatika Dan Teknik Elektro Terapan, 10(1), 34–40. https://doi.org/10.23960/jitet.v10i1.2262

Erfina, A. (2023). Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language. 2–11. https://doi.org/10.56294/dm202345

Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter. Smatika Jurnal, 10(02), 71–76. https://doi.org/10.32664/smatika.v10i02.455

Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 4(2), 113. https://doi.org/10.26418/jp.v4i2.27526

Leippold, M. (2023). Thus spoke GPT-3: Interviewing a large-language model on climate finance. Finance Research Letters, 53(December 2022), 103617. https://doi.org/10.1016/j.frl.2022.103617

Maulana, A. (2023). Perguruan Tinggi Perlu Sikapi Peluang dan Risiko ChatGPT bagi Pembelajaran. https://www.unpad.ac.id/2023/02/perguruan-tinggi-perlu-sikapi-peluang-dan-risiko-chatgpt-bagi-pembelajaran/

Nugroho, A. (2018). Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 2(2), 200. https://doi.org/10.30645/j-sakti.v2i2.83

Samsir, Ambiyar;, Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. Jurnal Media Informatika Budidarma, 5(1), 149. https://doi.org/10.30865/mib.v5i1.2604

Savitri, D. (2023). Praktisi AI Ungkap Peluang & Risiko Penggunaan ChatGPT di Perguruan Tinggi. Detik.Com. https://www.detik.com/edu/perguruan-tinggi/d-6610414/praktisi-ai-ungkap-peluang--risiko-penggunaan-chatgpt-di-perguruan-tinggi

Shaji George, A., Hovan George, A., & Martin, Asg. (2023). Partners Universal International Innovation Journal (PUIIJ) A Review of ChatGPT AI’s Impact on Several Business Sectors. February, 9–23. https://doi.org/10.5281/zenodo.7644359

Syarifuddinn, M. (2020). Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn. INTI Nusa Mandiri, 15(1), 23–28. https://doi.org/10.33480/inti.v15i1.1347

Yusuf, L., & Masripah, S. (2023). Pra-processing Text Sentimen analisis chatGPT dengan Algoritma Naïve bayes dan Optimasi PSO.

Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., Peng, H., Li, J., Wu, J., Liu, Z., Xie, P., Xiong, C., Pei, J., Yu, P. S., & Sun, L. (2023). A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT. 1–99. http://arxiv.org/abs/2302.09419

Zhu, J. J., Jiang, J., Yang, M., & Ren, Z. J. (2023). ChatGPT and Environmental Research. Environmental Science and Technology, 1–4. https://doi.org/10.1021/acs.est.3c01818

Published
2023-08-03
How to Cite
Yusuf, L., & Masripah, S. (2023). SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO. INTI Nusa Mandiri, 18(1), 59 - 64. https://doi.org/10.33480/inti.v18i1.4230
Article Metrics

Abstract viewed = 605 times
PDF downloaded = 565 times