DECISION SUPPORT SYSTEM FOR IDENTIFYING CHILDREN WITH SPECIAL NEEDS USING THE CLUSTER ANALYSIS METHOD

This research aims to develop a decision support system based on the K-Means Clustering method aimed at parents in recognizing the signs of children with special needs. Many parents do not understand the characteristics of children with special needs, causing delays in providing appropriate support and treatment [1]. This system functions to group children's data based on measurable characteristics, such as behavior patterns, cognitive abilities, and motor development, making it easier for parents to recognize children's needs more objectively. The K-Means Clustering method is used to group children's data into several clusters, each of which shows a certain pattern and level of special needs. The system is developed using MATLAB [3], with intuitive visualization results to help parents understand their child's condition. Thus, this system is expected to provide initial recommendations and encourage parents to consult further with professionals. Experimental results show that this system is able to classify data accurately and provide useful information as a first step for parents in recognizing their child's special needs.