Korpus.id: A Database-Driven Approach to Linguistic Annotation and Analysis

Indonesia's vast linguistic diversity, encompassing over 2,400 regional languages, is under threat due to globalization and declining number of speakers. The preservation and digitization of these languages are critical to maintaining cultural heritage and enabling AI integration. This paper introduces Korpus.id, a database-driven framework designed to address the limitations of existing annotation tools in managing multilingual and low-resource languages. Using a schema-based relational database, Korpus.id ensures scalability, consistency, and support for complex annotation across languages. Cross-language mapping and user-specific workflows facilitate independent and collaborative annotation, enabling the generation of high-quality linguistic data for AI and NLP applications. Korpus.id bridges the gap between traditional linguistic resources and modern AI readiness through its hierarchical design and multilingual schema organization, offering a robust platform for preserving endangered languages.