<![CDATA[Aim: Indonesian, as a national language, contains intricate linguistic features such as agglutinative morphology, idioms, and numerous dialectal variations. These characteristics present significant challenges in developing humanoid robots capable of natural interaction through Natural Language Processing (NLP). This study aims to address these linguistic complexities while exploring the entrepreneurial potential of localized NLP applications in Indonesia. Methods: The research employs a qualitative literature review method, focusing on existing studies related to Indonesian NLP datasets, transformer-based language models, and speech technologies. Key sources include IndoNLI for inference, IndoSentiment for sentiment analysis, and case studies of humanoid robots like Lumen. The analysis also includes approaches utilizing Big Data, multi-pass decoders, and contextual language modeling to optimize performance in Indonesian linguistic settings. Findings: Findings indicate that the successful development of Indonesian-speaking humanoid robots relies on context-aware NLP models trained on representative, culturally relevant datasets. Integrating multimodal systems and Big Data enables enhanced comprehension of idiomatic, regional, and informal expressions. The research also reveals that NLP-based innovations can be commercialized through AI-powered assistants, educational bots, and digital customer service, opening new opportunities for tech-driven entrepreneurship. Significance: This study contributes to both technological advancement and business innovation by linking linguistic AI research with entrepreneurial applications. It underscores the importance of building a robust local data ecosystem and designing language models that reflect Indonesia’s linguistic diversity. These insights are vital not only for improving human-robot interaction but also for fostering sustainable digital entrepreneurship within emerging markets like Indonesia.]]>
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