Backpropagation Model in Predicting the Location of Prospective Freshman Schools for Promotion Optimization

Authors

  • Muhammad Fahrur Rozi STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Dedy Hartama STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Ika Purnama Sari STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Rafiqa Dewi STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Zulia Almaida Siregar STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i1.161

Keywords:

ANN, Predictions, Freshman Schools , Promotion Optimization

Abstract

In carrying out promotions, it is also necessary to pay for the manufacture of brochures, banners and other promotional media to provide information to prospective students and attract prospective students to register. Determining the location of the promotion is one of the success factors in promotional activities. In this study, the Artificial Neural Network will be used to predict the location of the promotion. Backpropagation is one of the best artificial neural network methods used for prediction, this method is widely used by researchers in predicting a problem. The data analysis tool used is Matlab or what we call the (Matrix Laboratory) which is a program to analyze and compute numerical data, and Matlab is also an advanced mathematical programming language, which was formed on the premise of using the properties and forms of matrices. From the results of the algorithm used, it is expected to get good accuracy results with some architectural experiments later. So that this research can be an indicator to optimize promotions in the following year in order to attract prospective students to register for AMIK and STIKOM Tunas Bangsa Pematangsiantar

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Published

2022-03-18

How to Cite

Rozi, M. F., Hartama, D., Sari, I. P., Dewi, R., & Siregar, Z. A. (2022). Backpropagation Model in Predicting the Location of Prospective Freshman Schools for Promotion Optimization. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(1), 23–30. https://doi.org/10.55123/jomlai.v1i1.161

Issue

Vol. 1 No. 1 (2022): March

Section

Articles

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Copyright (c) 2022 Muhammad Fahrur Rozi, Dedy Hartama, Ika Purnama Sari, Rafiqa Dewi, Zulia Almaida Siregar

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