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

Sasmoko Buddhtha; Christina Natasha; Edy Irwansyah; Widodo Budiharto

doi:10.2991/ijcis.d.191101.003

<Previous Article In Issue

Next Article In Issue>

Volume 12, Issue 2, 2019, Pages 1575 - 1584

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

Authors

Sasmoko Buddhtha¹^{, 2}^{, *}, Christina Natasha³, Edy Irwansyah³, Widodo Budiharto³

¹Primary Teacher Education Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia 11480

²Research Interest Group in Educational Technology, Bina Nusantara University, Jakarta, Indonesia 11480

³Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480

^*Corresponding author. Email: sasmoko.36@gmail.com

Corresponding Author

Sasmoko Buddhtha

Received 27 May 2019, Accepted 14 August 2019, Available Online 15 November 2019.

DOI: 10.2991/ijcis.d.191101.003 How to use a DOI?
Keywords: Artificial neural networks; Backpropagation; Stochastic learning; Steepest gradient descent; Indonesian Teacher Engagement Index; Executive information system
Abstract: 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.
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/).

Download article (PDF)
View full text (HTML)

<Previous Article In Issue

Next Article In Issue>

Journal: International Journal of Computational Intelligence Systems
Volume-Issue: 12 - 2
Pages: 1575 - 1584
Publication Date: 2019/11/15
ISSN (Online): 1875-6883
ISSN (Print): 1875-6891
DOI: 10.2991/ijcis.d.191101.003 How to use a DOI?
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

ris enw bib

TY  - JOUR
AU  - Sasmoko Buddhtha
AU  - Christina Natasha
AU  - Edy Irwansyah
AU  - Widodo Budiharto
PY  - 2019
DA  - 2019/11/15
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  - 10.2991/ijcis.d.191101.003
ID  - Buddhtha2019
ER  -

download .riscopy to clipboard