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All Journal Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
AlHakim, Abdu Malik
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM) AlHakim, Abdu Malik; Atika, Prima Dina; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/96sfw544

Abstract

The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Analisis Sentimen Terhadap Bullying Di Indonesia Pada Twitter Menggunakan Naïve Bayes dan SVM AlHakim, Abdu Malik; Leonardo D.P, Harun; Putri, Alifia Nursyahrani
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.155

Abstract

Bullying has become a serious social problem in Indonesia. In the past few years, bullying cases are increasing, especially among children and adolescents. Bullying can occur anywhere, including at home, work, community, social media, and school, but it is most common in educational settings. Twitter or "X" is the most used social media in Indonesia, often a place for people to express their opinions on bullying. This research aims to analyze sentiment towards bullying in Indonesia through comments or tweets collected from Twitter using Naïve Bayes and Support Vector Machine (SVM) methods. From the analysis of 330 tweets, the Naïve Bayes method showed an accuracy of 77.27%, while the SVM method showed an accuracy of 72.72%.
Co-Authors Herlawati Herlawati Leonardo D.P, Harun Prima Dina Atika Putri, Alifia Nursyahrani