Adaptive Retrieval-Augmented Generation with Domain Specific Fine Tuning For Smart MSME Digital Transformation - Mawar Hardiyanti; Sri Hartati Wijono; Dwi Poetra Sedjati (Politeknik Negeri Batam, 2026)
Micro, Small, and Medium Enterprises (MSMEs) play a significant role in the Indonesian economy; however, the adoption of digital technologies among MSMEs remains limited, reducing operational efficiency and business competitiveness. This study proposes an Adaptive Retrieval-Augmented Generation (ARAG) framework integrated with WhatsApp to support MSME digital transformation through contextual conversational AI assistance. The proposed system combines adaptive retrieval mechanisms with domain-specific fine-tuning using IndoBERT and a knowledge base containing 50,000 MSME operational documents. A mixed-methods approach was employed, consisting of system development, comparative evaluation, and field validation involving 200 MSMEs. Experimental results demonstrated that ARAG achieved an average response accuracy of 86.80%, outperforming rule-based, TF-IDF, and generic large language model baselines. The system also achieved a Retrieval Precision@5 of 0.874, an end-to-end F1-score of 0.841, and a lower hallucination rate compared to generic LLM approaches. Field validation showed a 22.7% improvement in operational efficiency, a 17.4% increase in digital adoption rates, and a System Usability Scale (SUS) score of 84.6, categorized as excellent usability. The findings indicate that retrieval grounding and domain adaptation contribute substantially to improving contextual relevance and practical usability in MSME-oriented conversational AI systems. Therefore, the proposed ARAG framework demonstrates strong potential as a practical and scalable digital assistance solution for supporting Indonesian MSME digital transformation.