Data Mining Dalam Pengelompokkan Intelligence Quotient (IQ) Pada Anak Reterdasi Mental Dengan Menggunakan Algoritma K-Means
Abstract
Traditional methods for classifying children with mental retardation based on fixed IQ score thresholds are often inadequate in capturing the diversity of intellectual abilities. This study proposes the use of data mining techniques, specifically the K-Means clustering algorithm, to group Intelligence Quotient (IQ) data derived from psychological assessments. The research methodology consists of data collection, data preprocessing, selection of the optimal number of clusters, and implementation of the K-Means algorithm. The experimental results demonstrate that the proposed approach can successfully cluster IQ data into multiple groups representing distinct levels of intellectual functioning. The resulting clusters can be utilized as a decision-support mechanism to assist educators and practitioners in selecting appropriate instructional methods and intervention strategies in the field of special education.
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