Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Cici Suhaeni
Statistics and Data Science, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
EVALUATING ROBERTA AND GPT-BASED MODELS FOR SDG MULTICLASS TEXT CLASSIFICATION ACROSS DIFFERENT DOCUMENT LENGTHS Uswatun Hasanah; Agus Mohamad Soleh; Cici Suhaeni; Anwar Fitrianto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2645-2664

Abstract

Multiclass text classification remains a difficult task, primarily due to semantic ambiguity and differences in input length. This study evaluates RoBERTa and GPT-based models for multiclass text classification, focusing on how prompting strategies and document length affect accuracy and robustness. Experiments were conducted using the OSDG Community Dataset, which contains approximately 15,000 labeled samples. The dataset was partitioned into four subsets based on input length: short, medium, long, and all combined. Three GPT variants (zero-shot, few-shot, and fine-tuned) were compared against a RoBERTa baseline. Fine-tuning was implemented via OpenAI’s supervised API with prompt-response formatting. Performance was assessed through F1-score, precision, recall, and balanced accuracy. Fine-tuned GPT achieved the strongest results in all settings, with a macro F1-score of 0.9204 on the all-combined dataset, representing a 4.61% improvement over RoBERTa. Consistent gains were also observed across short (8.63%), medium (3.83%), and long (20.31%) texts. The largest improvement occurred on long documents, while medium-length inputs provided the most stable performance across models. These findings highlight the effectiveness of task-specific fine-tuning in enhancing GPT’s capability to classify SDG-related texts across diverse input lengths.
Co-Authors Agus Mohamad Soleh Anwar Fitrianto Uswatun Hasanah