<![CDATA[University collaboration plays a critical role in enhancing institutional quality and supporting global sustainability agendas. However, many higher education institutions face challenges in managing Memorandum of Understanding (MoU), Memorandum of Agreement (MoA), and Implementation Agreement (IA) documents, particularly in monitoring implementation and assessing their alignment with sustainability goals. This study introduces a University Collaboration Information System enhanced with IndoBERT-based Natural Language Processing (NLP) to automate sustainability impact assessment. A synthetic corpus of 30 annotated collaboration documents was developed, covering multi-label Sustainable Development Goals (SDG) classification and span-level Named Entity Recognition (NER). Two approaches were evaluated: (1) baseline TF-IDF + Support Vector Machine (SVM) for SDG classification and rule-based NER, and (2) fine-tuned IndoBERT for both tasks. Experimental results show that IndoBERT significantly outperforms the baselines, achieving an average F1-score of 0.93 for SDG classification (+16.3%) and 0.96 for NER (+18.5%). The system integrates these models to generate automated entity extraction, sustainability dashboards, and document monitoring features. This work contributes to the advancement of informatics by demonstrating the effectiveness of Transformer-based NLP in processing institutional documents and by providing an integrated information-system framework that strengthens the role of NLP within the field of computer science.]]>
