Developing RBL-STEM Learning Materials to Improve Students' Forecasting Skills in Smart OMP Placement Using Resolving Dominating Set Technique

This study developed Research-Based Learning integrated with STEM (RBL-STEM) learning materials to improve undergraduate students' forecasting skills in solving Smart OMP placement problems on electrical networks using the Resolving Dominating Set (RDS) technique. The study employed a development research design based on the 4D model, consisting of define, design, develop, and disseminate stages. The learning materials included a semester learning plan, student task design, student worksheets, and learning outcome tests. The trial involved 40 fifth-semester students enrolled in a Graph Applications course in the Mathematics Education Study Program at Universitas Jember. Data were collected through expert validation sheets, observation sheets, student activity records, learning outcome tests, and student response questionnaires. The validation results showed an average score of 3.61, equivalent to 90.29%, indicating that the materials were valid. Reliability testing produced a Cronbach's alpha value of 0.929, indicating high consistency of the test instrument. The practicality score from classroom implementation reached 3.82, equivalent to 95.63%. The effectiveness indicators were also achieved: 36 out of 40 students (90%) met the minimum mastery criterion, student activity reached 95.60%, and positive student responses reached 91.33%. These findings suggest that RBL-STEM learning materials situated in the Smart OMP and RDS context are valid, practical, and effective for strengthening students' forecasting skills in graph-based electrical network problems.