THE TITLE OF THE MANUSCRIPT DATA MINING FOR DETERMINING QUALIFICATION LEVEL OF THE HIGH SCHOOL INSTITUTIONS

Abstract

This study focuses on the use of data mining methods in educational classification. The heterogeneity within a group of schools can influence the achievement level of individual students and the overall evaluation of the school. One approach to improving the average school performance is by clustering schools with similar characteristics based on criteria such as accreditation, facilities, and performance. The resulting clusters are expected to minimize significant gaps, ensuring that the members of each group have relatively equal potential. By utilizing data mining clustering, a set of student groups with comparable abilities can be identified, allowing for targeted interventions. The results of this study will provide each school with information about their ranking. This method is used to classify and rank the data of schools. The ranking classification is performed using the SAW (Simple Additive Weighting) algorithm, with the highest performance scores.