Comparative Analysis of SVM and IndoBERT for Intent Classification in Indonesian Overtime Chatbots

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.