Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Abstract

Government agencies are required to mobilize every aspect of publication, which is carried out every year and must be accounted for and also carried out for each device that receives it, such as assisted villages by utilizing available APBD funds in maximizing work programs designed so that they can be implemented optimally and effectively. Getting the best from all aspects of the work program implementation, of course, there are important points in designing an annual work program without exception. Data mining itself can help the department of population, family planning, women’s em powerment, and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. This study aimed to build a classification model by adding a sigmoid activation function that used SVM and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results were used to get the best value for classifying the best P2KBP3A work program dataset, where it can be seen that the average accuracy value was 87.5%, the f1 value was 82.2%, the precision value was 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value.