BERT with Entity Recognition for Classifying Local and Import Products to Support Local MSMEs in the Global Market
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Abstract
The digital era and the COVID-19 pandemic have encouraged a shift toward online shopping. E-commerce has expanded the market by giving buyers access to a global market, resulting in increased cross-border transactions. As a direct challenge for local micro, small, and medium enterprises (MSMEs), the government has made regulations and campaigns to prioritize local products. This study presents a machine-learning model for classifying local products. We fine-tuned pre-trained Bidirectional Encoder Representations from Transformers (BERT) trained on Bahasa on product titles and used Entity Recognition to extract brand names as additional features. The model performs nearly perfectly with an accuracy of 97.79%. Adding brand name information provides an excellent signal to classify local products, indicated by the improvement after adding the brand name as a feature. With this implementation, the government and all e-commerce in Indonesia can collaborate to support the government campaign to encourage the competitiveness of MSMEs by prioritizing local products, reducing the number of imported products, and evaluating government programs specifically aimed at accelerating Indonesian MSMEs.
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