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All Journal Jurnal Sistem dan Informatika ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia JPIn : Jurnal Pendidik Indonesia Jurnal Bumigora Information Technology (BITe) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Ilmiah Pengabdian dan Inovasi Seminar Ilmiah Nasional Teknologi, Sains, dan Sosial Humaniora (SINTESA) Explore
Mardedi, Lalu Zazuli Azhar
Universitas Bumigora
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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Sentiment Classification of Football Supporters Using NusaBERT Embeddings, BiLSTM, and BiGRU Methods Guntara, Muhammad; Gusti Ayu Diah Gita Kartika Santi, I; Hairani, Hairani; Lalu Zazuli Azhar Mardedi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6151

Abstract

Class imbalance and the use of non-standard language in football supporters’ opinions on social media constitute major obstacles to producing accurate sentiment classification for evaluating federation performance. This study aims to identify the most effective bidirectional recurrent architecture for capturing public opinion after applying data balancing techniques. Using a primary dataset of 1,039 instances (604 positive and 435 negative samples), the proposed method integrates a pre-trained NusaBERT model with hybrid Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) layers. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training data, with dataset partitioning using a stratified split ratio of 70:30. The results indicate that the NusaBERT-BiLSTM model achieves the best performance, with a testing accuracy of 70.83% and an F1-score of 0.6990, outperforming the BiGRU variant, which attains an accuracy of only 64.74%. Furthermore, NusaBERT-BiLSTM demonstrates greater reliability in detecting negative sentiment, achieving a recall value of 0.6336 compared to 0.4504 for BiGRU. In conclusion, combining NusaBERT's semantic strength with SMOTE-based balancing and BiLSTM layers significantly enhances the model’s sensitivity to minority opinions without causing data leakage. This study contributes a more objective classification model for national team management to accurately map public criticism and aspirations on social media.
Co-Authors Apriani Apriani Ariyanto Ariyanto Baiq Chandra Herawati Dedi Aprianto Dyah Susilowati Eria Raudhatul Jannah Fadli, M. Najmul Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Guterres, Juvinal Ximenes Hairani Hairani HARYONO I Nyoman Miyarta Yasa I Putu Hariyadi Indah Puji Lestari Kartarina, Kartarina Khairan marzuki Lalu Ganda Rady Putra Miftahul Jihad Miftahul Madani Muhammad Haris Nasri Muhammad Idham Kholid Muhammad Innuddin Muhammad Ridho Hansyah Pahrul Irfan Rahmawati, Lela Regina Aprilia Riberu Riska Haerani Sasih Gunalan Setiawan, Pandu Dwi Sunardy Kasim Sutarman Sutarman Syahrir, Moch. Usman Ibnu Affan Wira Hendri