Comparative Analysis of Support Vector Machine And Perceptron Algorithms In Classification Of The Best Work Programs In P2KBP3A

Abstract

With the rapid growth of government agencies that are required to carry out an activity in every aspect that publishes and carries out obligations every year, it is required to be held accountable and also implemented for every device that receives such as fostered villages by utilizing the available APBD funds to maximize the work program that has been designed. so that it can be implemented as much as possible. That way, to get the best from all aspects of every work program implementation, there must be an important point from the annual work program design that is made without exception. Data mining itself can help P2KBP3A in analyzing each work program that is designed before being implemented in the future for the annual work program by looking at various aspects of past work program data and grouping work programs in the form of classification. In designing the work program, this research builds a classification model by adding a sigmoid activation function that uses SVM and perceptron to compare the accuracy results of the algorithm used to get the best work program design. From the various classifications used, the best value for classifying the dataset of the best P2KBP3A work programs can be seen from the average accuracy value of 87.5%, F1 value of 82.2%, the precision value of 80.2%, and recall value of 87.5%