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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJIET (International Journal of Indonesian Education and Teaching) Indonesian Journal of Information System Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Applied Sciences and Smart Technologies Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat
Hari Suparwito
Universitas Sanata Dharma
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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SVM and Ensemble Majority Voting Algorithm on Sentiment Analysis of Using chatGPT in Education Yayukristi Weko, Hildegardis; Suparwito, Hari
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.12680

Abstract

The pros and cons of using ChatGPT in education have caused academic debate as it has influenced current educational praxis. Discussions about the possibility of ChatGPT for writing manuscripts or doing assignments are rife on social media, one of which is Twitter. The purpose of this study is to understand the public perception of the use of ChatGPT in education. The proposed method is sentiment analysis with SVM and Majority Voting algorithms. SVM is one of the superior algorithms in pattern recognition and is suitable for use in classification. The Majority Voting ensemble algorithm combines independent algorithms' prediction results. In this research, majority voting uses three base classifiers, namely Naïve Bayes, Random Forest, and KNN. The results of the study showed that the accuracy of SVM is 83.6% and Majority Voting is 85.4%, with the accuracy of the NB, RF, and KNN base classifiers of 76.82%, 80.91%, and 74.5%, respectively. This proved that the Majority Voting Ensemble is superior to individual algorithms with higher accuracy values. This follows the results of previous research, where the ensemble performs better than the individual algorithm. The accuracy values of SVM and the Ensemble Majority Voting models showed that both models could successfully classify sentiment on tweet data for using ChatGPT in education.
Co-Authors Agnes Maria Polina Anastasia Rita Widiarti Anastasia Rita Widiarti Christiyanti Aprinastuti, Christiyanti Haris Sriwindono Markus Budiraharjo Pinaryanto, Kartono Prakoso, Bernardus Hersa Galih Ridowati Gunawan Rosalia Arum Kumalasanti Sri Hartati Wijono Wiwien Widyastuti Yayukristi Weko, Hildegardis