DOI: https://doi.org/10.14710/jtsiskom.5.4.2017.147-152

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

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 under http://creativecommons.org/licenses/by-sa/4.0.

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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.
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Keywords: Artificial neural networks; genetic algorithm; lecturer performance assessment

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Section: Original Research Articles
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  1. Ruslan, “Kepuasan mahasiswa terhadap kinerja dosen,” Jurnal Ilmu Pendidikan, vol. 17, no. Oktober, pp. 230–237, 2016
  2. Sunyoto and A. Miftahudin, “Mengukur Kepuasan Mahasiswa Berdasarkan Kinerja Dosen Dalam Proses Perkuliahan (Studi Empiris di Universitas Muhammadiyah Purwokerto),” Sainteks, vol. 11, no. 2, pp. 36–55, 2014
  3. M. Chalaris, S. Gritzalis, M. Maragoudakis, C. Sgouropoulou, and A. Tsolakidis, “Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques,” Procedia - Social and Behavioral Sciences, vol. 147, pp. 390–397, 2014
  4. L. Atika, “Sistem Penunjang Keputusan Penilaian Kinerja Pemilihan Dosen Berprestasi Menggunakan Metode AHP,” J. Imiah Matrik, vol. 12, no. 3, pp. 1–10, 2010
  5. E. N. Wahyudi and A. Jananto, “Final Report Penilaian Kinerja Dosen oleh Mahasiswa pada Satu Periode Tahun Akademik menggunakan Teknik Klustering (Studi Kasus : Universitas Stikubank Semarang),” Dinamik-Jurnal Teknologi Informasi, vol. 18, no. 2, pp. 101–111, 2013
  6. A. Izzuddin, “Optimasi Cluster pada Algoritma K-Means dengan Reduksi Dimensi Dataset Menggunakan Principal Component Analysis untuk Pemetaan Kinerja Dosen,” Energy, vol. 5, no. 2, pp. 41–46, 2015
  7. R. S. Zebulum, M. Vellasco, K. Guedes, and M. A. Pacheco, “Short-Term Load Forecasting Using Neural Nets,” in International Workshop on Artificial Neural Networks, Springer, Berlin, Heidelberg, 1995, pp. 1001-1008
  8. R. E. Abdel-Aal and A. Z. Al-Garni, “Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis,” Energy, vol. 22, no. 11, pp. 1059–169, 1997
  9. T. Park, J.-H. Lee, and B. Choi, “Optimization for Artificial Neural Network with Adaptive inertial weight of particle swarm optimization,” in the 2009 8th IEEE Int. Conf. Cogn. Informatics, 2009, pp. 481–485
  10. G. Wu, Y. Ren, Y. Li, H. Kwak, and S. Jang, “Research on Parameter Optimization of Neural Network,” International Journal of Hybrid Information Technology, vol. 2, no. 1, pp. 81–90, 2009
  11. R. L. Haupt and S. E. Haupt, Algorithms Practical Genetic Algorithms. John Wiley & Sons, 2004
  12. M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,” Eeccis, vol. 7, no. 1, pp. 59–64, 2013
  13. A. Yuliana and D. B. Pratomo, “Algoritma Decision Tree (C4.5) untuk Memprediksi Kepuasan Mahasiswa Terhadap Kinerja Dosen Politeknik TEDC Bandung,” Semnasinotek, vol. 1, no. 1, pp. 365–372, 2017
  14. S. Lestari and A. Suryadi, “Model Klasifikasi Kinerja Dan Seleksidosen Berprestasi Dengan Algoritma C. 45,” in Prosiding Seminar Bisnis & Teknologi, 2014, pp. 15–16

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