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All Journal Jurnal Sains dan Teknologi
Andrew Ryandi, Freddy
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Computer Science & IT Economics, Econometrics & Finance Mathematics Physics Social Sciences

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Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan Naive Bayes dan SVM Andrew Ryandi, Freddy; Pratiwi, Dian; Sari, Syandra
Jurnal Sains dan Teknologi Vol. 7 No. 1 (2025): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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Abstract

This study examines public sentiment on social media platform X regarding Indonesia's Ministry of Health during the COVID-19 pandemic, using Naïve Bayes and Support Vector Machine (SVM) algorithms. Posts mentioning the Ministry’s official account (@KemenkesRI) were preprocessed and labeled using the VADER tool. Sentiment classification was performed with TF-IDF word weighting, and both algorithms were evaluated. Results show SVM achieved slightly higher accuracy (79%) than Naïve Bayes (77%), indicating its effectiveness in handling complex language structures, though it requires more computational resources. This research underscores the utility of SVM for analyzing public sentiment on health policies..
Co-Authors Dian Pratiwi Sari, Syandra