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All Journal INOVTEK Polbeng - Seri Informatika Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Restikom : Riset Teknik Informatika dan Komputer West Science Social and Humanities Studies Jurnal Pengabdian Kepada Masyarakat Indonesia
Ata Amrullah
Universitas Islam Darul Ulum Lamongan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Journal : Unisda Journal of Mathematics and Computer Science (UJMC)

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Analisis Sentimen Publik terkait Migrasi Tenaga Kerja Indonesia di Platform X menggunakan SVM-IndoBERT Ata Amrullah
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 1 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i1.9891

Abstract

Diverse public opinions on social and economic issues related to labor migration are often expressed on the social media platform X (Twitter). This research aims to classify public sentiment toward this phenomenon by analyzing tweets containing the hashtag "#KaburAjaDulu". Sentiment classification is performed by comparing two Support Vector Machine (SVM) approaches that utilize indoBERT embeddings, a language model designed to capture the nuances of the Indonesian language. Both SVM models are trained using web crawling data from the X platform, with the main difference lying in the application of hyperparameter tuning on one of the models. The data collected through web crawling from the X platform then undergoes a pre-processing stage that includes text normalization and stopword removal. The results show that the SVM model optimized through hyperparameter tuning achieved an accuracy of 90.5%, higher than the SVM model without tuning which achieved only 77.7%. This finding underscores the importance of hyperparameter tuning in improving the performance of sentiment classification models, especially when utilizing rich feature representations such as indoBERT embeddings to understand deeper language context.
Co-Authors Agung Muliawan Bambang Suharto Idris Winarno Jamilatul Badriyah Khanun Roisatul Ummah Loso Judijanto M Udin Harun Al Rasyid, M Udin Harun Miko Tri Afandi Ramadhani, Nilam Siti Alfiatur Rohmaniah