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All Journal INFOKUM LOFIAN: Jurnal Teknologi Informasi dan Komunikasi
Erwin Daniel Sitanggang
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Computer Science & IT

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Analysis of Elearning Quality Measurement With Webqual Method at Politeknik MBP Medan Erwin Daniel Sitanggang; Maradu Sihombing; Maranata Pasaribu; Beny Irawan
INFOKUM Vol. 10 No. 1 (2021): Desember, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

The development of information technology today has also changed the lifestyle and way of life of many people. By using information technology, many people can explore the world without being limited by space and time through the internet. Information technology has become a tool that has the power as a driving force that changes business, economics, socio-politics and other fields without limits. One of the things that has experienced major changes in lifestyle and way of life in the field of education is teaching and learning activities. The Politeknik MBP Medan college, which was also affected by Pandami, had to change the teaching-learning method using the Learning Management System service which is the right teaching-learning method to provide learning materials to students, in this case called students. In order to obtain the quality of this service, first, the satisfaction level of service users is measured based on the standard of comparison (gap) between reality and the expectations of users of the service. Based on the results of quality measurements from e-learning at the Politeknik MBP Medan for the academic year 2020/2021 in the even semester using the Webqual method, it was concluded that the Webqual method along with its attributes can provide analytical results to be used in improving the performance quality of e-learning. The results of measuring the quality of the data processing of respondents' answers are obtained that the User Satisfaction Level Score is -0.18, this indicates that the quality of e-learning as a whole is not in line with the expectations of users. The results of the analysis of 22 statement attributes from the Webqual method show that all attributes get the results of the "Tingkatkan" pattern analysis and none of the statement attributes get the "Pertahankan" pattern.
Analysis of Elearning Quality Measurement with Webqual method using Artificial Neural Networks Erwin Daniel Sitanggang; Misdem Sembiring; Anjar Pinem; Maranata Pasaribu
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Currently, artificial intelligence is a concern for the world because of its increasingly rapid and sophisticated application in helping humans to complete their work in everyday life. One of the widely used methods is artificial neural networks that are part of deep learning and a subsection of machine learning. In its network training, the data used as input is the gap score of each webqual dimension and the data used as the output is the gap score of the average webqual attributes of each respondent. The training process is expected to produce an actual output close to the predetermined target output, resulting in the best model of artificial neural networks with feedforward backpropagation algorithms. From the results of the training experiment, the best model of artificial neural network architecture was obtained with a feedforward backpropagation algorithm at the time of training from 174 data to be able to replace the Webqual method in this study using the 3-20-1 model and the algorithm used was Levenberg-Marquardt (trainln). Where there is 1 Input layer with 3 neuron units, 1 hidden layer with 20 neuron units and 1 Output layer with 1 neuron unit with a mean square error (mse) of 0.00000000000721 and regression of 1 or 100%. And after testing using 58 data using the network configuration obtained during training, the results of the comparison between the network output and the target were 100% accurate.
Co-Authors Anjar Pinem Beny Irawan, Beny Gija, Permana Maradu Sihombing Maranata Pasaribu Maranata Pasaribu Misdem Sembiring