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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika dan Rekayasa Perangkat Lunak
Jessicha Putrianingsih Pamput
Universitas Negeri Makassar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Evaluating Synonym Augmentation Impact on SBERT Performance for Indonesian Social Media Style Classification Jessicha Putrianingsih Pamput; Aindri Rizky Muthmainnah; Dewi Fatmarani Surianto; Nur Azizah Eka Budiarti; Abdul Wahid
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2580

Abstract

Language on social media reflected the identity and characteristics of its users, including differences in language style between generations. Millennials and Generation Z were two dominant demographic groups in digital communication that exhibited linguistic variations, which often caused gaps in understanding during online interactions. Variations in language structure and expression posed challenges in understanding the context of cross-generational communication. Therefore, this study aimed to classify linguistic styles across generations in social media texts by combining Sentence-BERT (SBERT). FastText-based synonym augmentation in Indonesian, and Support Vector Machine (SVM) as a margin-based classification model that utilizes embedding representations from SBERT. The results showed that synonym augmentation improved model accuracy from 85% to 93%, with a similarity threshold of 0.7 providing the best balance between data diversity and semantic consistency. These findings confirmed that synonym-based augmentation and SBERT semantic adaptation were effective in capturing generational linguistic differences in informal Indonesian. This approach had the potential to be applied in other NLP tasks that required contextual understanding of social language variation, such as sentiment analysis and cross-generational dialect detection.
Integrasi Metode Weighted Product (WP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk Pendukung Keputusan Penentuan Asisten Dosen Aindri Rizky Muthmainnah; Jessicha Putrianingsih Pamput; Fhatiah Adiba; Dewi Fatmarani Surianto; Asmaul Husnah Nasrullah; Nur Azizah Eka Budiarti
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

The selection of teaching assistants requires an objective and effective decision-making system. This study designs a decision support system for selecting assistants in the Algorithm and Basic Programming course at JTIK, Universitas Negeri Makassar, by integrating the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. WP assigns weights to evaluation criteria, while TOPSIS identifies the best candidates based on positive and negative ideal distances. The criteria include academic performance, communication, subject mastery, and teaching experience. Testing results show that the system produces consistent selections, aligned with manual calculations and recruitment outcomes, proving its validity and effectiveness in supporting the selection process.
Co-Authors Abdul Wahid Aindri Rizky Muthmainnah Asmaul Husnah Nasrullah Dewi Fatmarani Surianto Fhatiah Adiba