Performance of Logistic Regression and Multilayer Perceptron Neural Network in Classification of Objects

E. Fajariyanto¹ , M. Fajar²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.52-55, Feb-2021

Online published on Feb 28, 2021

Copyright © E. Fajariyanto, M. Fajar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Employment is a problem that always gets serious attention from the government. Problems with the quality of human resources, motivation, and work culture are influencing factors in labor force problems. Classification becomes important as a means of evaluation and concluding labor force problems. The classification method itself consists of the conventional method (require assumptions) and the robust method (does not require assumptions. This study uses several classification methods including the logistic regression, the backpropagation algorithm, and the backpropagation algorithm with the addition of momentum. The results of this study show that based on simulation, the backpropagation algorithm and the backpropagation with the addition of momentum results in a higher classification accuracy than the logistic regression, the existence of momentum can increase the accuracy of the backpropagation classification with the addition of momentum.

Key-Words / Index Term :
Performance, Logistic Regression, Neural Network, Classification

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