The Integration of the RBL-STEM Learning Model and Graph Theory in Solving Transportation and Logistics Optimization Problems to Enhance Students' Computational Thinking Skills

Abstract This study aims to integrate the Research-Based Learning (RBL) model with the STEM approach and graph theory in solving transportation and logistics optimization problems to enhance students’ computational thinking skills. The model is designed to train students in identifying transportation and logistics issues, exploring solutions based on graph theory, and analyzing and optimizing distribution routes using algorithms such as Dijkstra and Floyd-Warshall. This research employs a narrative qualitative method, analyzing research-based learning through six RBL-STEM phases: problem identification, initial solution exploration, data collection, data analysis, result interpretation, and model presentation. The results show that this approach significantly improves students’ skills in problem decomposition, pattern recognition, algorithmic thinking, and abstraction in solving real-world problems. Concretely, students demonstrated an average score improvement of 23% in computational thinking indicators after participating in the learning process, particularly in designing optimal route graph models and constructing algorithmic logic for route selection. Moreover, the use of technologies such as Google Maps and graph modeling software enables students to perform real-time data-driven analysis. Therefore, this study proves that the integration of RBL-STEM and graph theory not only enhances conceptual understanding of transportation and logistics optimization but also equips students with applicable computational thinking skills for real-world contexts. This learning model can serve as a reference in developing more innovative and industry-relevant STEM-based teaching strategies. Keywords: RBL-STEM; Graph; Transportation; Logistics; Computational Thinking Skill.