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All Journal JURNAL SISTEM INFORMASI BISNIS JOIV : International Journal on Informatics Visualization Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal of Information Systems and Informatics Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi Akuntansi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Media Computer Science Paradigma Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Jurnal Infortech
Galih Setiawan Nurohim
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Other

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Enhancing Javanese Emotion Classification: A Comparative Study of Cross-Lingual, Supervised, and Hybrid Transfer Learning using IndoBERTweet Galih Setiawan Nurohim; Heribertus Ary Setyadi; Sigit Wahyudi; Paulus Tofan Rapiyanta
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1657

Abstract

This research investigates transfer learning efficacy for five-class emotion classification in Javanese Ngoko. A parallel Indonesian–Javanese Ngoko corpus was synthesized by translating 5,400 samples from the PRDECT-ID dataset using machine translation, with translation quality verified via a preliminary expert validation sample. Using IndoBERTweet as the backbone architecture, three paradigms were evaluated: zero-shot transfer (E1), fully supervised learning (E2), and cross-lingual transfer learning (E3) with identical hyperparameters. Empirical results indicate that the cross-lingual transfer (E3) setup achieved peak performance (67,5% accuracy; 0,67 weighted F1) under the evaluated dataset and experimental setting. Per-class analysis identified that positive affect (Happy) showed cross-lingual stability, whereas negative emotions (Sadness, Fear) suffered degradation due to lexical divergence between the two languages. Training dynamics revealed early-onset overfitting, suggesting model capacity exceeds current dataset density. This work establishes a baseline benchmark for Javanese emotion classification and provides a reproducible machine-translated parallel corpus, emphasizing the need for future validation with native-speaker data to mitigate translation bias.
Co-Authors ., Supriyanta Ahmad Fauzi Ahmad Fauzi Akbar, Muhammad Faitullah Al Amin, Budi Amin, Budi Al Annida Purnamawati Budi Al Amin Candra Agustina Denny Irawan Diah Pradiatiningtyas Doddy Satrya Perbawa Eka Rahmawati eka rahmawati Fanny Fatma Wati Fauzi Ahmad Muda Fauzi, Ahmad Heribertus Ary Setyadi Indradewa, Rhian Muhammad Faittullah Akbar Paulus Tofan Rapiyanta Pudji Widodo Pudji Widodo Sigit Wahyudi Siti Khotimah Sri Rejeki Sundari Sundari Supriyanta Supriyanta Sutanto Sutanto Sutanto Sutanto Wahyuningsih, Hartati Dyah Wawan Haryanto Wawan Nugroho Yusuf Sutanto Yusuf Sutanto Zainal Muttaqin