Penerapan Principal Component Analysis pada Model Deteksi Dini Anak Autisme - Saifudin, Aries; Nirmala, Endar; Kusyadi, Irpan (Jurnal Teknologi Sistem Informasi dan Aplikasi, 2024)
ASD (Autism Spectrum Disorder) is a neurological disorder that causes lifelong disturbances in children resulting in mental illness. Treatment can help but cannot be cured. Currently ASD is detected by understanding a child's behavior and intellectual activity. This diagnosis can be subjective, time-consuming, inconclusive, does not provide precise insight into genetics and is unsuitable for early detection. In Autism, a major challenge faced in many healthcare conditions is timing of diagnosis. It can take up to 6 months to diagnose a child with autism with certainty because of the lengthy process, and a child must see many different specialists to diagnose autism, from a developmental pediatrician, neurologist, psychiatrist or psychologist. Machine Learning Methods can make relevant changes to speed up the process. In this study, it is proposed to apply PCA (Principal Component Analysis). PCA is basically the basis of multivariate data analysis that applies the projection method. This analysis technique is usually used to summarize multivariate data tables on a large scale so that they can be used as a collection of smaller variables or a summary index. From there, variables are then analyzed to find out certain trends, variable clusters, and outliers. In this study it is proposed to implement the PCA (Principal Component Analysis) algorithm, namely PCA (Principal Component Analysis), Kernel PCA, Sparse PCA, and Incremental PCA. In this study using the experimental method by making applications to implement the proposed algorithm. Then test the model using the secondary dataset and measure the performance of the model. The research results show that the model that applies Sparse PCA gives the best results, which means that the application of PCA can be used to reduce the number of features and increase model performance.