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All Journal JOIV : International Journal on Informatics Visualization Journal of Robotics and Control (JRC) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems DECODE: Jurnal Pendidikan Teknologi Informasi Journal of Software Engineering and Information System (SEIS) Jurnal Riset Sistem dan Teknologi Informasi Jurnal Pengabdian kepada Masyarakat (PEMAS)
Dianda Rifaldi
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Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Riset Sistem dan Teknologi Informasi

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Evaluasi Sentimen Pengguna ChatGPT Menggunakan Naive Bayes: Tinjauan dari Confusion Matrix dan Classification Report Dianda Rifaldi; Tri Stiyo Famuji; Bella Okta Sari Miranda; Fauzan Purma Ramadhan; Iriene Putri Mulyadi; Vanji Saputra6; Fanani, Galih Pramuja Inngam
Jurnal Riset Sistem dan Teknologi Informasi Vol. 3 No. 2 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v3i2.1990

Abstract

The development of artificial intelligence (AI) technology, particularly in natural language processing (NLP), has led to various innovations, including ChatGPT. Its growing popularity highlights the need for user sentiment analysis. This study evaluates user sentiment toward ChatGPT using the Naive Bayes algorithm. The dataset, obtained from Kaggle, consists of 500 labeled English tweets categorized as positive, neutral, or negative. The process involved text preprocessing, TF-IDF feature extraction, data splitting (80% training, 20% testing), and model training. The results show an accuracy of 56%, with the highest f1-score in the negative class (0.67) and the lowest in the neutral class (0.38). The model exhibits classification imbalance, with high precision but low recall in the neutral class, and high recall but low precision in the positive class. The confusion matrix further confirms frequent misclassifications between classes. These findings reflect the limitations of Naive Bayes in handling contextual relationships in text data. Improvements can be achieved through data balancing, enhanced NLP-based feature representation, and the application of more complex classification algorithms.
Co-Authors Abdul Fadlil Abdul Fadlil Abdul Fadlil Alfian Ma’arif Asno Azzawagama Firdaus Bella Okta Sari Miranda Fanani, Galih Fanani, Galih Pramuja Inngam Fauzan Purma Ramadhan Furizal, Furizal Herman Herman, - Iriene Putri Mulyadi Mulyadi, Iriene Putri Murinto Ramadhan, Fauzan purma Saputra, Vanji Tri Stiyo Famuji Tristanti, Novi Vanji Saputra Vanji Saputra6