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ANALISIS SENTIMEN KINERJA TENAGA MEDIS INDONESIA MENGGUNAKAN MODELING ROBERTA DAN METODE MACHINE LEARNING

Edu Elektrika Journal · Vol. 13 No. 1 (2026) · pp. 1–8

DOI
10.15294/eduel.v13i1.22163
ISSN
2723-5602, 2252-7095
Published
2026-01-19
Publisher
LPPM Universitas Negeri Semarang
Language
eng

Abstract

The development of information technology has led to the emergence of various social media applications, such as X, which allow users to share information and opinions. However, social media can also function as a platform for the spread of hoaxes and hate speech. One of the challenges faced is determining whether user comments are negative, neutral, or positive through sentiment analysis. This study aims to compare the performance of various classification algorithms in sentiment analysis of medical personnel services in X, using the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms with RoBERTa model-based labeling. Data was collected through the tweet-harvest library and processed using the Python programming language. The results showed a significant increase in accuracy, with the model able to classify public opinion into positive, negative, or neutral categories. The SVM model achieved the highest accuracy of 91.8%, outperforming other models such as Random Forest and Naïve Bayes, and provided insight into sentiment towards government health services. These findings provide valuable insights for policymakers in improving the provision of health services and managing public perceptions of medical personnel.

Keywords

Collaboration network of the authors

6 authors · 9 co-authorship links

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  • Universitas Sumatera Utara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Muhammad ZarlisUniversitas Sumatera Utara1
Herman MawengkangUniversitas Sumatera Utara1
Syahril EfendiUniversitas Sumatera Utara1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/eduel/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">ANALISIS SENTIMEN KINERJA TENAGA MEDIS INDONESIA MENGGUNAKAN MODELING ROBERTA DAN METODE MACHINE LEARNING</dc:title>
	<dc:creator xml:lang="en">Husaini, Rahayun Amrullah</dc:creator>
	<dc:creator xml:lang="en">Priyanto, Dadang</dc:creator>
	<dc:creator xml:lang="en">Hendro Martono, Galih</dc:creator>
	<dc:subject xml:lang="en">Analisis Sentimen</dc:subject>
	<dc:subject xml:lang="en">Tenaga Medis</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">RoBERTa</dc:subject>
	<dc:description xml:lang="en">The development of information technology has led to the emergence of various social media applications, such as X, which allow users to share information and opinions. However, social media can also function as a platform for the spread of hoaxes and hate speech. One of the challenges faced is determining whether user comments are negative, neutral, or positive through sentiment analysis. This study aims to compare the performance of various classification algorithms in sentiment analysis of medical personnel services in X, using the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms with RoBERTa model-based labeling. Data was collected through the tweet-harvest library and processed using the Python programming language. The results showed a significant increase in accuracy, with the model able to classify public opinion into positive, negative, or neutral categories. The SVM model achieved the highest accuracy of 91.8%, outperforming other models such as Random Forest and Naïve Bayes, and provided insight into sentiment towards government health services. These findings provide valuable insights for policymakers in improving the provision of health services and managing public perceptions of medical personnel.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-01-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/eduel/article/view/22163</dc:identifier>
	<dc:identifier>10.15294/eduel.v13i1.22163</dc:identifier>
	<dc:source xml:lang="en">Edu Elektrika Journal; Vol. 13 No. 1 (2025); 1-8</dc:source>
	<dc:source>2723-5602</dc:source>
	<dc:source>2252-7095</dc:source>
	<dc:source>10.15294/eduel.v13i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/eduel/article/view/22163/4461</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Edu Elektrika Journal</dc:rights>
</oai_dc:dc>