Comparison of Support Vector Machine and Extreme Learning Machine for Sentiment Analysis of Free Nutritious Meal Program on X Application

Rizqi Aulia Wijayanti; Arief

doi:10.15294/ujm.v14i2.27822

Jurnal

Comparison of Support Vector Machine and Extreme Learning Machine for Sentiment Analysis of Free Nutritious Meal Program on X Application

Unnes Journal of Mathematics · Vol. 14 No. 2 (2026) · hlm. 49–57

DOI: 10.15294/ujm.v14i2.27822
ISSN: 2460-5859, 2252-6943
Terbit: 2026-10-21
Penerbit: Universitas Negeri Semarang
Bahasa: eng

Abstrak

In the current digital era, data analysis has become a critical field, particularly in processing unstructured text data from online interactions. A prime example is public sentiment analysis on platforms like Application X, which is valuable for evaluating government policy effectiveness. However, the diversity of opinions and the unstructured nature of text data pose challenges in achieving accurate classification. This study aims to implement and compare the performance of Support Vector Machine (SVM)—known for its effectiveness in high-dimensional data—and Extreme Learning Machine (ELM)—renowned for computational efficiency—in sentiment classification. The dataset consists of tweets from the X social media platform regarding the Free Nutritious Meal Program. We evaluated four SVM kernel types and four ELM activation functions. Results demonstrate that the linear-kernel SVM achieved the highest accuracy (88.4%) with an F1-gap of 5.82%, while the linear-activation ELM attained 83.8% accuracy with a 2.54% F1-gap. These findings suggest that linear-kernel SVM is more suitable for sentiment analysis of similar datasets. Moreover, the sentiment analysis revealed a predominance of negative sentiment, reflecting public concern and criticism regarding the program's implementation.

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Rekan penulis	Organisasi	Artikel bersama
Ahmad Saeroji	Afiliasi tidak diketahui	1
Aulia Rizqi Nugrahaeni	Afiliasi tidak diketahui	1
R. Pazla	Afiliasi tidak diketahui	1
N. Jamarun	Afiliasi tidak diketahui	1
Elihasridas	Afiliasi tidak diketahui	1
G. Yanti	Afiliasi tidak diketahui	1
E. M. Putri	Afiliasi tidak diketahui	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en">Comparison of Support Vector Machine and Extreme Learning Machine for Sentiment Analysis of Free Nutritious Meal Program on X Application</dc:title>
	<dc:creator xml:lang="en">Rizqi Aulia Wijayanti</dc:creator>
	<dc:creator xml:lang="en">Arief</dc:creator>
	<dc:subject xml:lang="en"> sentiment analysis</dc:subject>
	<dc:subject xml:lang="en">support vector machine</dc:subject>
	<dc:subject xml:lang="en">extreme learning machine</dc:subject>
	<dc:subject xml:lang="en">MBG</dc:subject>
	<dc:description xml:lang="en">In the current digital era, data analysis has become a critical field, particularly in processing unstructured text data from online interactions. A prime example is public sentiment analysis on platforms like Application X, which is valuable for evaluating government policy effectiveness. However, the diversity of opinions and the unstructured nature of text data pose challenges in achieving accurate classification. This study aims to implement and compare the performance of Support Vector Machine (SVM)—known for its effectiveness in high-dimensional data—and Extreme Learning Machine (ELM)—renowned for computational efficiency—in sentiment classification. The dataset consists of tweets from the X social media platform regarding the Free Nutritious Meal Program. We evaluated four SVM kernel types and four ELM activation functions. Results demonstrate that the linear-kernel SVM achieved the highest accuracy (88.4%) with an F1-gap of 5.82%, while the linear-activation ELM attained 83.8% accuracy with a 2.54% F1-gap. These findings suggest that linear-kernel SVM is more suitable for sentiment analysis of similar datasets. Moreover, the sentiment analysis revealed a predominance of negative sentiment, reflecting public concern and criticism regarding the program's implementation.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-10-21</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/ujm/article/view/27822</dc:identifier>
	<dc:identifier>10.15294/ujm.v14i2.27822</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 14 No. 2 (2025): Unnes Journal of Mathematics Volume 2, 2025; 49-57</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v14i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/27822/11863</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Unnes Journal of Mathematics</dc:rights>
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