Analisis Sentimen Terhadap Aplikasi Chatgpt Pada Twitter Menggunakan Algoritma Naïve Bayes

Muhamad Ilmar Rifaldi(1*), Yudhi Raymond Ramadhan(2), Irsan Jaelani(3),

(1) Sekolah Tinggi Teknologi Wastukancana Purwakarta, Jawa Barat, Indonesia
(2) Sekolah Tinggi Teknologi Wastukancana Purwakarta, Jawa Barat, Indonesia
(3) Sekolah Tinggi Teknologi Wastukancana Purwakarta, Jawa Barat, Indonesia
(*) Corresponding Author

Abstract


Sentiment analysis or opinion mining is the detection of attitudes, opinions and emotions towards an object. The value of sentiment analysis can be divided into 2 types of sentiment, namely positive and negative sentiment. One of the topics that is currently being discussed by the public on Twitter social media is an AI-based chatbot application, ChatGPT. This research makes public tweets on Twitter as sentiment analysis material. Data retrieval is done using the Twitter API with data processing on Rapidminer and visualization using Power BI. The sentiment analysis process uses the Naïve Bayes algorithm by dividing the data of 301 tweets into training data and test data. Then testing using Confusion matrix to calculate the performance of the classification results. From the test results, 80% accuracy was obtained, then the Precission value was 80.95% and the Recall value 89.47%. It can be concluded that public sentiment on Twitter for the ChatGPT application tends to be positive seen from the number of positive tweet data as much as 74%.

Full Text:

PDF

References


Mukrodin and N. Mega Sasmita, “rtificial Inteligence Dalam Apilkasi Chatbot Sebagai Helpdesk Obyek Wisata Dengan Permodelan Natural Language Processing (Studi Kasus: Kabupaten Cilacap),” Smart Comp Jurnalnya Orang Pint. Komput., vol. 10, no. 1, pp. 7–14, 2021, doi: 10.30591/smartcomp.v10i1.2135.

A. Setiawan and U. K. Luthfiyani, “Penggunaan ChatGPT Untuk Pendidikan di Era Education 4.0: Usulan Inovasi Meningkatkan Keterampilan Menulis,” J. PETISI, vol. 04, no. 01, 2023, [Online]. Available: https://chat.openai.com.

N. Sucahyo, I. Kuniati, and K. Harvit, “Analisis Sentimen Masyarakat Terhadap UU Cipta Kerja Pada Media Sosial Twitter,” vol. 02, no. 01, 2022.

E. Undamayanti et al., “Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 2, pp. 916–930, 2022.

T. N. Wijaya, R. Indriati, and M. N. Muzaki, “Analisis Sentimen Opini Publik Tentang Undang-Undang Cipta Kerja Pada Twitter,” Jambura J. Electr. Electron. Eng., vol. 3, no. 2, pp. 78–83, 2021, doi: 10.37905/jjeee.v3i2.10885.

A. Anugrah, T. I. Hermanto, and I. Kaniawulan, “Sentiment Analysis Of Internet Service Providers Using Naïve Bayes Based On Particle Swarm Optimization,” J. Ris. Inform., vol. 4, no. 4, pp. 371–378, 2022, doi: 10.34288/jri.v4i4.408.

A. Nabillah, S. Alam, and M. G. Resmi, “Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm,” vol. 3, no. 1, pp. 14–27, 2022.

A. E. Augustia, R. Taufan, Y. Alkhalifi, and W. Gata, “Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization,” Paradig. - J. Komput. dan Inform., vol. 23, no. 2, pp. 158–166, 2021, doi: 10.31294/p.v23i2.10430.

D. A. Wulandari and R. Saedudin, Rd. Rohmat Andreswari, “Analisis Sentimen Media Sosial Twitter Terhadap Reaksi Masyarakat Pada Ruu Cipta Kerja Menggunakan Metode Klasifikasi Algoritma Naive Bayes Analysis,” vol. 8, no. 5, pp. 9007–9016, 2021.

N. Helmiah et al., “Penerapan Metode Naïve Bayes dalam Analisis Persepsi Masyarakat mengenai Rencana Pengesahan RUU Omnibus Law di Bidang Investasi dan Ketenagakerjaan Tahun 2020 di Indonesia,” J. MSA ( Mat. dan Stat. serta Apl. ), vol. 8, no. 2, p. 48, 2020, doi: 10.24252/msa.v8i2.16743.

I. K. Syahputra, F. A. Bachtiar, and S. A. Wicaksono, “Implementasi Data Mining untuk Prediksi Mahasiswa Pengambil Mata Kuliah dengan Algoritme Naive Bayes,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5902–5910, 2018, [Online]. Available: http://j-ptiik.ub.ac.id./index.php/j-ptiik/article/view/3464

I. G. N. Daffa Adnyana, F. Adams, and A. Windari Oktavia, “Analisis Sentimen Terhadap Undang-Undang Cipta Kerja Menggunakan Metode Naïve Bayes,” Semin. Nas. Mhs. Ilmu Komput. dan Apl. Jakarta-Indonesia, no. September, pp. 120–129, 2021.

D. Siti Utami and A. Erfina, “Analisis Sentimen Objek Wisata Bali Di Google Maps Menggunakan Algoritma Naive Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 418–427, 2022.

Y. Nurdiansyah, F. Rahman, and P. Pandunata, “Analisis Sentimen Opini Publik Terhadap Undang-Undang Cipta Kerja pada Twitter Menggunakan Metode Naive Bayes Classifier,” Pros. Semin. Nas. Sains Teknol. dan Inov. Indones., vol. 3, no. November, pp. 201–212, 2021, doi: 10.54706/senastindo.v3.2021.158.




DOI: http://dx.doi.org/10.30645/j-sakti.v7i2.687

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats