Implementation of Tracer Study Data Classification at the Universitas Muhammadiyah Sumatera Utara by Utilizing Data Mining Using a Combination of Support Vector Machine and Neural Network Algorithms
Keywords:
Classification;, Support Vector Machine;, Neural Network;, Tracer Study ;, Data Mining;Abstract
Universities routinely conduct Tracer Studies every year with the aim of implementing accreditation data needs at the University of Muhammadiyah North Sumatra using data mining techniques. This study focuses on the use of a combination of Support Vector Machine (SVM) and Neural Network algorithms to optimize Tracer Study data classification. SVM is used to solve non-linear classification problems by finding the optimal hyperplane, while Neural Network is used to model and recognize complex patterns in the data. The research methodology includes Tracer Study data collection, data preprocessing, SVM and Neural Network model training, and evaluation of classification results. The results show that the combination of SVM and Neural Network significantly improves the accuracy and reliability of Tracer Study data classification, providing valuable insights for academic policy makers and curriculum developers at the University of Muhammadiyah North Sumatra.
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