Fine-Tuning a Large Language Model on Vertex AI for a New Student Registration Chatbot at Universitas Muhammadiyah Makassar - Desi Anggreani; Muhyiddin A M Hayat; Lukman; Ahmad Faisal; Khadijah; Darniati (Indonesian Journal of Data and Science, 2026)
This study addresses the limitations of manual admission services at Universitas Muhammadiyah Makassar, which often result in delayed and inconsistent information delivery. To overcome these challenges, an institution-specific chatbot was developed by fine-tuning the Gemini 2.5 Flash model on the Google Cloud Vertex AI platform. The model was trained using a curated domain-specific dataset of 1,430 question–answer pairs derived from official documents and frequently asked questions. The fine-tuning process employed supervised learning to enhance contextual relevance and response accuracy. System performance was evaluated using automated text quality metrics, achieving an average BLEU score of 0.23526 and a ROUGE-L Recall score of 0.53424, indicating satisfactory lexical and semantic similarity. Furthermore, a user acceptance evaluation involving 52 respondents yielded a Customer Satisfaction Score (CSAT) of 84.2%, reflecting high user satisfaction. These results demonstrate that fine-tuning a Large Language Model (LLM) for specific institutional needs effectively improves both response quality and service reliability. Ultimately, this approach offers a practical and scalable solution for modernizing student admission services in higher education, ensuring that prospective students receive accurate information in a timely and efficient manner.