<![CDATA[The advancement of digital technology requires higher education institutions to provide academic information services that are fast, accurate, and easily accessible to students. The Faculty of Economics and Business at Satya Wacana Christian University (FEB UKSW) still faces limitations in providing responsive, always-available information services. This study aims to develop and evaluate a chatbot system as an efficient solution for academic information services. The research method employed is the Software Development Life Cycle (SDLC) using the Waterfall model, which consists of the stages of requirements analysis, system design, implementation, testing, and evaluation. The chatbot system was developed using Node.js for the backend and HTML, CSS, and JavaScript for the frontend. The dataset was constructed based on the following categories: academic services, financial information, promotion and cooperation, data and information center, and IT services. The testing results indicate that the chatbot achieves an average response time of 0.002 seconds and an accuracy rate of 95% for questions that are similar to those in the dataset. This study concludes that the developed chatbot effectively improves the speed and efficiency of academic information services, although further development is required to enhance its understanding of user queries.Kata kunci: Chatbot; Academic Information Services; Artificial Intelligence; Node.js; Natural Language Processing.AbstrakKemajuan teknologi digital menuntut perguruan tinggi untuk menyediakan layanan informasi akademik yang cepat, akurat, dan mudah diakses oleh mahasiswa. FEB UKSW masih menghadapi keterbatasan media layanan informasi yang responsif dan tersedia sepanjang waktu. Penelitian ini bertujuan untuk mengembangkan serta mengevaluasi sistem chatbot sebagai solusi layanan informasi akademik yang efisien. Metode penelitian yang digunakan adalah Software Development Life Cycle (SDLC) dengan model Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan evaluasi. Sistem chatbot dikembangkan menggunakan Node.js sebagai backend serta HTML, CSS, dan JavaScript pada sisi frontend. Dataset disusun berdasarkan kategori layanan akademik, keuangan, promosi dan kerja sama, pusat data dan informasi, serta layanan IT. Hasil pengujian menunjukkan chatbot memiliki waktu respons rata-rata 0,002 detik dan tingkat akurasi sebesar 95% untuk pertanyaan yang memiliki kemiripan dengan dataset. Penelitian ini menyimpulkan bahwa chatbot efektif meningkatkan kecepatan dan efisiensi layanan informasi akademik, meskipun masih perlu pengembangan pada pemahaman pertanyaan. ]]>
