Authors:

Uli Rindu Debora, I Putu Agus Eka Pratama, Gusti Made Arya Sasmita

Abstract:

“Cases of domestic violence (KDRT) always attract numerous public comments on Twitter’s social media platform. This research aims to conduct a sentiment analysis classification regarding ongoing cases of KDRT on Twitter. The study employs the Multinomial Naive Bayes and SVM algorithms to test accuracy in classifying tweets. The research methodology includes the following steps: data collection from Twitter, data preprocessing, sentiment analysis, sentiment classification using SVM and Multinomial Naïve Bayes algorithms, and analysis of results from both algorithms. The research findings indicate that the SVM algorithm achieves the highest accuracy rate, reaching 73% at an 80:20 ratio. In comparison, the Multinomial Naïve Bayes algorithm attains an accuracy rate of 70% at the same ratio. Therefore, it can be concluded that the SVM algorithm exhibits better accuracy compared to the Multinomial Naïve Bayes algorithm.”

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References

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PDF:

https://jurnal.harianregional.com/jitter/full-110096

Published

2024-01-02

How To Cite

DEBORA, Uli Rindu; PRATAMA, I Putu Agus Eka; SASMITA, Gusti Made Arya. Sentiment Analysis of Domestic Violence Issues on Twitter Using Multinomial Naïve Bayes and Support Vector Machine.JITTER : Jurnal Ilmiah Teknologi dan Komputer, [S.l.], v. 4, n. 3, p. 1992-2000, jan. 2024. ISSN 2747-1233. Available at: https://jurnal.harianregional.com/jitter/id-110096. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JTRTI.2023.v04.i03.p06.

Citation Format

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Issue

Vol 4 No 3 (2023): JITTER, Vol.4, No.3 December 2023

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License