Syahroni Hidayat, Budi Sunarko, Uswatun Hasanah
Intelligence is the ability to think, experience learning, problem-solving, and adaptation to new situations. Thus, intelligence is an essential foundation in the learning process. Logical-mathematical intelligence (KLM) is a strong indicator for assessing individual intelligence levels and learning achievements. KLM significantly correlates to the ability to solve story problems (KSC). However, exploring these two variables to group students to help educators prepare good learning strategies has not yet been done. Therefore, this study applied the K-means algorithm to reveal it all by utilizing KLM and KSC data. Silhouette index (SI) and Davies-Bouldin (DBI) were used to validate the results of the K-means cluster. The results show that K-means can classify students based on their KLM and KSC data into three groups: high, medium, and low. The SI and DBI values are 0.46 and 0.75, respectively. It indicates the formed clusters are quite good and there is still an opportunity for improvement. © 2025 by IGI Global Scientific Publishing. All rights reserved.
Universitas Negeri Semarang, Indonesia
