ANALISIS KEMAMPUAN PEMECAHAN MASALAH MATEMATIKA MATERI BANGUN DATAR SISWA KELAS V SEKOLAH DASAR - Rahayu, Salamah Tri; Pramudyanti, Pramudyanti; Perdana, Ryzal (Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, 2024)
The purpose of this study is to characterize and understand the mathematical problem-solving skills of fifth-grade students at SDN 1 Tanjung Sari, particularly with regard to narrative problems with flat forms. This study employs a qualitative descriptive methodology that focuses on close observation and in-depth examination of the research participants, who are SDN 1 Tanjung Sari fifth-grade pupils. Two primary methods were used in this study to gather data: assessments of students' mathematical problem-solving skills and interviews. The Miles and Huberman Model, which has three primary stages data reduction, data presentation, and conclusion drawing was used to conduct the data analysis. The technique of eliminating extraneous or useless data is known as data reduction. Data presentation is the process of arranging and displaying information in a way that is simple to understand. Drawing conclusions from study results entails analyzing data and drawing judgments. The study's findings indicate that pupils' average proficiency in solving mathematical problems involving flat shapes is 75.78, falling within the middle range. This indicates that although there is still need for development, pupils' grasp of the notion of flat forms is adequate. The study indicated that, when considering learning interest, students' average mathematical problem-solving skill was 87.22 for high learning interest students, 74.85 for moderate learning interest students, and 57.80 for low learning interest students. This demonstrates that students' aptitude for solving mathematical puzzles is greatly influenced by their learning interests. Overall, this study offers significant new information on how learning interests might influence students' ability to solve mathematical problems. Future instructional materials for more efficient learning can be developed using these findings as a foundation