Please use this identifier to cite or link to this item: http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/5570
Title: Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs
Authors: Rosnelly, Rika
Keywords: Classification Government SVM Perceptron Sigmoid Work Program
Issue Date: Mar-2023
Series/Report no.: Vol. 22, No. 2;285∼298
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.
Description: Support vector machine, commonly abbreviated as SVM, is a method that solves problems such as predictions, whether in the form of classification or regression [1]. This algorithm has the advantage of high accuracy and does not require a lot of data samples to avoid overfitting [2]. This algorithm can also solve problems by using datasets that have a large feature space [3]. In addition to SVM, there is also another algorithm that is also good for use in prediction problems in the form of classification, namely the perceptron, a simple supervised learning neural network algorithm that can be used to recognize patterns in data [4]. As a method to enter into machine learning and focus on the type of supervised learning, it can be used to form behavioral patterns from data based on a collection of data samples that have been labeled [5]. By using the desired input and output values from a data set, supervised learning can be used to solve classification or regression problems depending on the data being processed [6]. As one part of supervised learning, the classification itself can be used to build a learning model, where the computer learns the input data and generates a classification function to categorize the data used as test material [7].
URI: http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/5570
ISSN: 2476-9843
Appears in Collections:A Paper



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