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All Journal CogITo Smart Journal
Syalom Gabriela Wagey
Universitas Klabat
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Research Project Topic Recommender System Using Generative Language Model Debby Erce Sondakh, S.Kom, M.T, Ph.D; Semmy Wellem Taju; Jian Kezia Tesalonica Yuune; Anjelita Ferensca Kaminang; Syalom Gabriela Wagey
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.678.654-666

Abstract

Education has become a driver of a person's continuous innovation to improve their quality. Currently, the use of artificial intelligence determines progress in education. In this research, artificial intelligence technology was applied to develop a web-based recommendation system to help students at the Faculty of Computer Science, Klabat University, choose appropriate research topics for their final assignments. To provide personalized and contextually relevant suggestions, the recommendation system leverages deep learning and generative language models, specifically GPT-3. The Rapid Application Development process model is employed to develop the system. Its key components include semantic search, rapid engineering, and an advanced vector database for effective data management and retrieval. The functions provided by the system include user account registration, login, input of major subject grades and research preferences, and personalized recommendation results. Some additional features such as profile management, previous recommendation history, and password reset options are also provided. All these functions have been tested using the black box method.
Co-Authors Anjelita Ferensca Kaminang Jian Kezia Tesalonica Yuune Semmy Wellem Taju Sondakh, Debby Erce