<![CDATA[The rapid increase in scientific publications has created significant challenges for researchers in finding relevant literature. Conventional citation-based recommender applications often have drawbacks, such as bias toward popular articles and vulnerability to manipulation through citation cartels, which reduce objectivity. To address these limitations, this development aimed to design and develop a web-based scientific article recommendation application using a hybrid recommender system approach. The development followed the waterfall methodology, covering requirements analysis, design, implementation, and testing stages. The hybrid approach combines Content-based filtering by analyzing content similarity and Collaborative filtering based on user interaction history. Scientific articles and user preferences were modeled in a graph database to map relationships, with the implementation of Graph Data Science Library using algorithms named K-Nearest Neighbor, Degree centrality, and PageRank. Based on 101 black-box unit test cases, the application successfully delivered three main recommendation features by integrating content analysis—based on access history and currently viewed articles—with user preference modeling through peer institutions. The testing results confirm that all recommendation functions operated as intended across various user scenarios. Overall, the developed application provides multiple recommendation features that enhance objectivity and relevance, supporting researchers, students, and practitioners in discovering]]>
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