<![CDATA[Digital transformation in higher education requires the development of intelligent and adaptive information systems, including services such as overtime submission for university staff. Chatbots offer a promising solution to enhance user interaction with the E-LEMBUR system. However, developing chatbots in academic settings poses challenges, including limited training data, complex overtime policies, and diverse institutional terminology. This study compares two intent classification approaches: Support Vector Machine (SVM), a traditional machine learning method, and IndoBERT, a transformer-based model designed for the Indonesian language. The dataset comprises 250 real user queries from the overtime system at Institut Teknologi Sepuluh Nopember (ITS). Experimental results show IndoBERT achieves 87% accuracy, slightly outperforming SVM at 85%. While IndoBERT offers better accuracy, it demands higher computational resources, presenting a trade-off between performance and efficiency. This study contributes by validating IndoBERT’s effectiveness on a limited dataset, establishing an initial benchmark for intent classification in overtime chatbots, and offering implementation recommendations aligned with university IT infrastructure. These findings lay the groundwork for developing context-aware information systems for staff services in Indonesian higher education.]]>
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