Abstract
This research aims to analyze the performance of the K-Means algorithm concerning the intensity of use of the Natural Sciences (IPA) laboratory and its impact on students' science skills. The study utilizes data on the intensity of science laboratory use and the results of science skills tests from a group of students. The analysis leverages the K-Means algorithm's ability to cluster students based on their laboratory use patterns and determine qualifications based on their science skills. The performance metrics employed in this study include evaluating cluster suitability and conducting correlation analysis between science laboratory use patterns and students' science skills. Therefore, the research focuses on demonstrating how the K-Means algorithm can effectively group data on the intensity of science laboratory use and its correlation with students' science skills.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
