Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy

This paper fine-tunes IndoBERT to automatically classify Indonesian elementary school exam questions according to Bloom's Taxonomy cognitive levels, achieving 97% accuracy. The system aims to automate the manual teacher task of categorizing questions by cognitive complexity (LOTS vs HOTS).

Background: The learning assessment of elementary schools has recently incorporated Bloom's Taxonomy, a structure in education that categorizes different levels of cognitive learning and thinking skills, as a fundamental framework. This assessment now includes High Order Thinking Skill (HOTS) questions, with a specific focus on Indonesian topics. The implementation of this system has been observed to require teachers to manually categorize or classify questions, and this process typically requir

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