Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS
- https://doi.org/10.2991/ijcis.d.191101.003How to use a DOI?
- Artificial neural networks, Backpropagation, Stochastic learning, Steepest gradient descent, Indonesian Teacher Engagement Index, Executive information system
Teacher engagement is a newly-emerged concept in the field of Indonesian teacher education. To support this concept, we designed an artificial neural network (ANN) using backpropagation, stochastic learning, and steepest gradient descent algorithms to determine teacher engagement based on the Indonesian Teacher Engagement Index (ITEI). The resulting ANN may be used in a data-gathering website for teachers to use for self-evaluation and self-intervention. The optimal architecture for the ANN has 44 input nodes, 26 first hidden layer nodes, 20 second hidden layer nodes, and 7 output nodes, with a learning rate of 0.05 and trained over 5000 iterations. The sample data used for training was gathered by ITEI researchers and the Executive Board of Indonesian Teachers Association (Pengurus Besar Persatuan Guru Republik Indonesia, PB-PGRI) and includes data of teachers from all around Indonesia. The maximum accuracy of this ANN was 97.98%. The sample data were then used to create an executive information system presented in the form of a map created using ArcGIS Pro software.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Sasmoko Buddhtha AU - Christina Natasha AU - Edy Irwansyah AU - Widodo Budiharto PY - 2019 DA - 2019/11 TI - Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS JO - International Journal of Computational Intelligence Systems SP - 1575 EP - 1584 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191101.003 DO - https://doi.org/10.2991/ijcis.d.191101.003 ID - Buddhtha2019 ER -