PENERAPAN KLASIFIKASI RANDOM FOREST TERHADAP DATA GANGGUAN SPEKTRUM AUTISME (ASD) PADA ANAK - ANAK MENGGUNAKAN SELEKSI FITUR PRINCIPAL COMPONENT ANALYSIS - Luthfiyah Amatullah, (2022)
Autistic Spectrum Disorder (ASD) is a complex and highly variable developmental disorder of brain function. This disorder significantly affects verbal, non-verbal communication and social interaction. General symptoms of this disorder will usually be seen in children starting from the age of two years. The purpose of this research is to apply the Principal Component Analysis (PCA) feature selection method to research data Autistic Spectrum Disorder Screening Data for Children Data Set obtained from the University of California Irvine (UCI) Machine Learning Data Repository where PCA serves to reduce or simplify dimensions data from the dataset and classify it using the Random Forest classification modeling. In addition, to find out how the results of the evaluation (confusion matrix) are and how the differences in the results of the evaluation are to datasets that use the Principal Component Analysis (PCA) feature selection method and those that do not use this method. Evaluation of the results of research on Autism Spectrum Disorder Data in Children using PCA feature selection, which resulted in an accuracy value of 98%, precision of 96%, recall of 100% and specificity of 96%. While the evaluation results without going through the PCA process first resulted in an accuracy value of 91%, precision of 92%, recall of 84% and specificity of 100%.