Peningkatan Akurasi Klasifikasi Tingkat Penguasaan Materi Bahan Ajar Menggunakan Jaringan Syaraf Tiruan Dan Algoritma Genetika

Improved Accuracy of Classification Comprehension Level of Teaching Materials Using Artificial Neural Networks and Genetic Algorithms

*Oman Somantri orcid  -  Department of Informatics, Politeknik Harapan Bersama Tegal, Indonesia
Slamet Wiyono  -  Department of Informatics, Politeknik Harapan Bersama Tegal, Indonesia
Received: 4 Sep 2017; Published: 26 Oct 2017.
Open Access Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer
License URL: http://creativecommons.org/licenses/by-sa/4.0

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Article Info
Section: Original Research Articles
Language: ID
In Volume 5, Issue 4, Year 2017 (October 2017)
Statistics: 749 916
Abstract
Decision support systems can be applied to perform a lecturer's performance assessment. This research aims to develop a hybrid model using the artificial neural network (ANN) and genetic algorithm (GA) that can be implemented and used as a model of decision support data analysis that produce better accuracy, specifically to assess the lecturer's comprehension of their teaching materials. The use of GA in determining the ANN parameter has increased the accuracy from 85.36% to 85.73%. The training cycle is also reduced to 624 from 1000. The use of this JST-GA model can be applied to provide a better lecture's performance assessment system.
Keywords: Artificial neural networks; genetic algorithm; lecturer performance assessment

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