Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Indonesian sentiment towards global economic recession in 2023 using optimized hyperparameters of support vector machine kernels

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4948–4956

DOI
10.11591/ijai.v13.i4.pp4948-4956
ISSN
2252-8938, 2089-4872
Terbit
2024-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The potential for the 2023 global recession has troubled people worldwide, particularly in light of the COVID-19 pandemic. This study employs a sentiment analysis approach to examine how the Indonesian internet community, particularly on Twitter, perceives the topics related to the global economic recession. We collected 11,017 uploaded tweets that were analyzed using support vector machine classifier with linear, radial basis function (RBF), sigmoid, and polynomial kernel schemes. Furthermore, we optimized the classifiers with C, Gamma, and degree hyperparameters. Empirical evidence indicates a lack of preparedness to face a global recession, evidenced by most responses towards 2023 global recession exhibiting concerns about high inflation and economic instability. The finding also suggests that the optimized RBF is a superior modeling kernel relative to others. Collectively, these results provide insights with significant implications for sentiment analysis, natural language processing, and the study of behavioural economics.

Kata kunci

Jaringan kolaborasi para penulis

30 penulis · 91 tautan kepenulisan bersama

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • University of Brawijaya
  • Universitas Muhammadiyah Jakarta
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
abu bakar sambahAfiliasi tidak diketahui3
asep awaludin prihantoAfiliasi tidak diketahui2
Abd. Aziz AminAfiliasi tidak diketahui2
Umbu Paru Lowu DawaAfiliasi tidak diketahui2
Ardiansyah KurniawanAfiliasi tidak diketahui1
Suci Puspita SariAfiliasi tidak diketahui1
Euis AsrianiAfiliasi tidak diketahui1
Refin SatrioAfiliasi tidak diketahui1
Henny PramoedyoUniversity of Brawijaya1
Moch. Dawam MaghfoerAfiliasi tidak diketahui1
Sisca FajrianiAfiliasi tidak diketahui1
Maharani Pertiwi KoentjoroAfiliasi tidak diketahui1
Ahmad Nuril Fuad Al FatihAfiliasi tidak diketahui1
Budiyanto BudiyantoUniversitas Muhammadiyah Jakarta1
Abdul Aziz JaziriAfiliasi tidak diketahui1
Muhammad Imam Syafi'iAfiliasi tidak diketahui1
Gatot ArdianAfiliasi tidak diketahui1
M. AmenanAfiliasi tidak diketahui1
Lutfi Ni'matus SalamahAfiliasi tidak diketahui1
Wahyu Budi SetiawanAfiliasi tidak diketahui1
Adi Tiya YanuarAfiliasi tidak diketahui1
Citra Satrya Utama DewiAfiliasi tidak diketahui1
Ekwan Nofa WiratnoAfiliasi tidak diketahui1
Lutfi Ni'matus SalamahAfiliasi tidak diketahui1
Frentina Murty SujadiAfiliasi tidak diketahui1
Khoirur Rizal LuthfiAfiliasi tidak diketahui1
Asep A. PrihantoAfiliasi tidak diketahui1

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) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Indonesian sentiment towards global economic recession in 2023 using optimized hyperparameters of support vector machine kernels</dc:title>
	<dc:creator>Maarif, Dairatul</dc:creator>
	<dc:creator>Aulia Hafizha, Adinda</dc:creator>
	<dc:creator>Kurniawan, Andi</dc:creator>
	<dc:subject xml:lang="en-US">Natural Language Processing; Machine Learning; International Political Economy</dc:subject>
	<dc:subject xml:lang="en-US">Global recession; Kernels; Optimized hyperparameters; Sentiment analysis; Support vector machine</dc:subject>
	<dc:description xml:lang="en-US">The potential for the 2023 global recession has troubled people worldwide, particularly in light of the COVID-19 pandemic. This study employs a sentiment analysis approach to examine how the Indonesian internet community, particularly on Twitter, perceives the topics related to the global economic recession. We collected 11,017 uploaded tweets that were analyzed using support vector machine classifier with linear, radial basis function (RBF), sigmoid, and polynomial kernel schemes. Furthermore, we optimized the classifiers with C, Gamma, and degree hyperparameters. Empirical evidence indicates a lack of preparedness to face a global recession, evidenced by most responses towards 2023 global recession exhibiting concerns about high inflation and economic instability. The finding also suggests that the optimized RBF is a superior modeling kernel relative to others. Collectively, these results provide insights with significant implications for sentiment analysis, natural language processing, and the study of behavioural economics.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Pembangunan Nasional Veteran Jakarta</dc:contributor>
	<dc:date>2024-12-01</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23708</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4948-4956</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4948-4956</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23708/14265</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>