<![CDATA[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.]]>
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