Development of Acehx for Sentiment Analysis Using a Bert-Based Model

In the digital era, sentiment analysis represents a significant domain within natural language processing (NLP). Nevertheless, research on Indonesian regional languages, particularly Acehnese, remains limited. The primary challenges involve the unavailability of representative datasets and the absence of BERT-based models optimized through the Masked Language Modeling (MLM) approach. Existing models, such as IndoBERT, are trained on Indonesian corpora and therefore fail to adequately capture the unique linguistic features of Acehnese. This study develops the AcehX Sentiment dataset and introduces the AcehXBERT model through pre-training IndoBERT-base with the MLM approach on the AcehX corpus. The model is then fine-tuned for Acehnese sentiment classification tasks. Experimental results show an F1-macro score of 82.50% on the AcehX Sentiment dataset and 81.89% on the NusaX Sentiment dataset, outperforming NusaBERT. These findings highlight the importance of adapting pretrained models and tokenizers for regional languages, while supporting the preservation and technological integration of the Acehnese language.