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Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University

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@article{IJASEIT11152,
   author = {Ainul Yaqin and Arif Dwi Laksito and Siti Fatonah},
   title = {Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {610--617},
   keywords = {Prediction; study period; neural network; informatics.},
   abstract = {The study period of the student in a tertiary institution is undoubtedly essential in implementing the objectives of the tertiary institution, particularly for the implementation of the study program, so that its outcomes will affect accreditation. Prediction of students' study period can be a reference for higher education institutions in making policies for the future. Based on XYZ University data, especially in the informatics study program, many students have the different generation and concentration therein. In the implementation of students in studying, several factors, including the value of the Grade Point Average (GPA), can affect the study period taken. Likewise, the institutions often do not understand the conditions or predictive value of students' study period on campus. The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 and 1 output target for the study period. Prediction results obtained the best results on the neuron network pattern 50 with 0.01 as a learning rate, which detail of MSE value, the training is 0,017516 and the testing is 0,047721, with an accuracy value of 77%.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11152},
   doi = {10.18517/ijaseit.11.2.11152}
}

EndNote

%A Yaqin, Ainul
%A Laksito, Arif Dwi
%A Fatonah, Siti
%D 2021
%T Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University
%B 2021
%9 Prediction; study period; neural network; informatics.
%! Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University
%K Prediction; study period; neural network; informatics.
%X The study period of the student in a tertiary institution is undoubtedly essential in implementing the objectives of the tertiary institution, particularly for the implementation of the study program, so that its outcomes will affect accreditation. Prediction of students' study period can be a reference for higher education institutions in making policies for the future. Based on XYZ University data, especially in the informatics study program, many students have the different generation and concentration therein. In the implementation of students in studying, several factors, including the value of the Grade Point Average (GPA), can affect the study period taken. Likewise, the institutions often do not understand the conditions or predictive value of students' study period on campus. The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 and 1 output target for the study period. Prediction results obtained the best results on the neuron network pattern 50 with 0.01 as a learning rate, which detail of MSE value, the training is 0,017516 and the testing is 0,047721, with an accuracy value of 77%.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11152
%R doi:10.18517/ijaseit.11.2.11152
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 2
%@ 2088-5334

IEEE

Ainul Yaqin,Arif Dwi Laksito and Siti Fatonah,"Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 610-617, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.11152.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Yaqin, Ainul
AU  - Laksito, Arif Dwi
AU  - Fatonah, Siti
PY  - 2021
TI  - Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 610
EP  - 617
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Prediction; study period; neural network; informatics.
N2  - The study period of the student in a tertiary institution is undoubtedly essential in implementing the objectives of the tertiary institution, particularly for the implementation of the study program, so that its outcomes will affect accreditation. Prediction of students' study period can be a reference for higher education institutions in making policies for the future. Based on XYZ University data, especially in the informatics study program, many students have the different generation and concentration therein. In the implementation of students in studying, several factors, including the value of the Grade Point Average (GPA), can affect the study period taken. Likewise, the institutions often do not understand the conditions or predictive value of students' study period on campus. The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 and 1 output target for the study period. Prediction results obtained the best results on the neuron network pattern 50 with 0.01 as a learning rate, which detail of MSE value, the training is 0,017516 and the testing is 0,047721, with an accuracy value of 77%.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11152
DO  - 10.18517/ijaseit.11.2.11152

RefWorks

RT Journal Article
ID 11152
A1 Yaqin, Ainul
A1 Laksito, Arif Dwi
A1 Fatonah, Siti
T1 Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 610
OP 617
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Prediction; study period; neural network; informatics.
AB The study period of the student in a tertiary institution is undoubtedly essential in implementing the objectives of the tertiary institution, particularly for the implementation of the study program, so that its outcomes will affect accreditation. Prediction of students' study period can be a reference for higher education institutions in making policies for the future. Based on XYZ University data, especially in the informatics study program, many students have the different generation and concentration therein. In the implementation of students in studying, several factors, including the value of the Grade Point Average (GPA), can affect the study period taken. Likewise, the institutions often do not understand the conditions or predictive value of students' study period on campus. The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 and 1 output target for the study period. Prediction results obtained the best results on the neuron network pattern 50 with 0.01 as a learning rate, which detail of MSE value, the training is 0,017516 and the testing is 0,047721, with an accuracy value of 77%.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11152
DO  - 10.18517/ijaseit.11.2.11152