Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM)
DOI:
https://doi.org/10.47065/bulletincsr.v5i2.475Keywords:
Sentiment Analysis; ChatGPT; Support Vector Machine; Social Media; TwitterAbstract
This study aims to analyze public sentiment toward OpenAI ChatGPT technology on Twitter using the Support Vector Machine (SVM) method. The background of this research is based on the increasing global use of the internet and artificial intelligence (AI), as well as the role of social media as a platform for people to express their opinions. This study employs a qualitative research approach using the Support Vector Machine method, with data collection conducted through primary data obtained by crawling data from Twitter. The research uses data collected from 4,305 Indonesian-language tweets gathered between January and September 2023. These tweets were then classified into positive, neutral, and negative sentiments using the SVM method. The results indicate that out of the total collected data, 2,196 tweets had a neutral sentiment, 1,500 tweets had a positive sentiment, and 591 tweets had a negative sentiment. In the model performance evaluation, training data with an 80:20 ratio achieved the highest accuracy of 94.25%, while testing data with a 70:30 ratio achieved the highest accuracy of 93.16%. Additionally, the use of 10-fold cross-validation on training data resulted in an accuracy of 89.94%, while testing data achieved an average accuracy of 78.17%.
Downloads
References
A. Erfina, “Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language Implementación del algoritmo de clasificación Naive Bayes para el análisis del sentimiento de los usuarios de Twitter e,” pp. 2–11, 2023, doi: 10.56294/dm202345.
R. Kajayaan, T. Putra, F. R. Saputro, L. Hakim, and Y. Ramadhan, “Fenomena ChatGPT?: Peningkatkan civic skill digital native generation,” vol. 2, no. 2, pp. 140–147, 2023.
A. Bin, M. Alkatiri, and A. N. S. Nasution, “OPINI PUBLIK TERHADAP PENERAPAN NEW NORMAL DI MEDIA SOSIAL TWITTER,” vol. 11, no. 1, 2020.
I. Engineering, M. Muadin, H. Asnal, and P. T. Informatika, “IMPLEMENTASI METODE SUPPORT VECTOR MACHINE PADA OPINION,” vol. 7, no. 1, 2023.
A. Witanti, “ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE ( SVM ),” vol. 5, no. 1, pp. 59–67, 2022.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
M. I. Petiwi, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine,” vol. 6, pp. 542–550, 2022, doi: 10.30865/mib.v6i1.3530.
M. R. Fauzan, H. O. L. Wijaya, S. Satrianansyah, and J. Karman, “ANALISIS SENTIMEN MASYARAKAT TERHADAP KENAIKAN HARGA BBM DI MEDIA SOSIAL TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE,” in Prosiding Seminar Riset Mahasiswa, 2023.
J. Teknologi, I. Jtsi, S. Informasi, F. Teknik, K. Universitas, and T. Indonesia, “TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN OPINI MASYARAKAT TERHADAP TEKANAN MENTAL PADA MEDIA SOSIAL TWITTER,” vol. 3, no. 4, 2022.
A. Alwasi’a, “Analisis Sentimen Pada Review Aplikasi Berita Online Menggunakan Metode Maximum Entropy (Studi Kasus: Review Detikcom pada Google Play 2019),” Skripsi, 2020.
M. Rahman Fauzan, H. Oktafia Lingga Wijaya, and J. Karman, “Analisis Sentimen Masyarakat Terhadap Kenaikan Harga Bbm Di Media Sosial Twitter Menggunakan Metode Support Vector Machine,” Semin. Ris. Mahasiswa-Computer Electr. (SERIMA-CE, vol. 1, no. 1, p. 82, 2023.
D. I. Komputer, F. Matematika, D. A. N. Ilmu, and P. Alam, “Analisis sentimen pada twitter menggunakan metode naïve bayes (studi kasus pemilihan gubernur dki jakarta 2017) muhammad hadiyan rasyadi,” 2017.
R. Vindua and A. U. Zailani, “Analisis Sentimen Pemilu Indonesia Tahun 2024 Dari Media Sosial Twitter Menggunakan Python,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 2, p. 479, 2023, doi: 10.30865/jurikom.v10i2.5945.
O. I. Gifari, M. Adha, I. R. Hendrawan, F. Freddy, and S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” vol. 2, no. 1, pp. 36–40, 2022.
N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 6, no. 3, pp. 150–155, 2021.
N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
A. W. V. Hutabarat, N. L. S. S. Adnyani, and K. Suryadi, “Analisis Sentimen Data Ulasan Pengguna MyPertamina di Twitter dengan Metode Text Mining,” J. Rekayasa Sist. Ind., vol. 13, no. 1, pp. 145–154, 2024, doi: 10.26593/jrsi.v13i1.6958.145-154.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM)
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Ayu Septini, Susanto, Elmayati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
