Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa

Heny Pratiwi(1*), Kusno Harianto(2),

(1) STMIK Widya Cipta Dharma
(2) STMIK Widya Cipta Dharma
(*) Corresponding Author

Abstract


Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are the value of the entrance examination for new students, the first grade IP value, Gender, and Working Status, while the output variable is the quality value as a classification of academic performance. The results of this study state that the Extreme Learning Machine algorithm has a 14.84% error rate lower than Backpropagation 28.20%. From the model testing stage, the most accurate result is the Extreme Learning Machine algorithm because it has the highest accuracy and the lowest error rate.

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References


ASIF, R., MERCERON, A., ALI, S. A., & HAIDER, N. G. 2017. Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194.

D. HUANG AND Z. WU, 2017. “Forecasting Outpatient Visits Using Empirical Mode Decomposition Coupled With Backpropagation Artificial Neural Networks Optimized by Particle Swarm Optimization,” PLoS One, vol. 12, no. 2, pp. 1–18.

EHRET, D. HOCHSTUHL, D. GIANOLA, AND G. THALLER, 2015. “Application of neural networks with back-propagation to genome-enabled prediction of complex traits in HolsteinFriesian and German Fleckvieh cattle,” Genetics Selection Evolution, vol. 47, no. 1, p. 22.

HERNÁNDEZ-LOBATO, José Miguel; ADAMS, 2015. Ryan. Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International Conference on Machine Learning. p. 1861-1869.

HUANG, Guang-Bin, et al. 2015. Local receptive fields based extreme learning machine. IEEE, 10.2: 18-29.

J. TARIGAN, NADIA, R. DIEDAN, AND Y. SURYANA, 2017. “Plate Recognition Using Backpropagation Neural Network and Genetic Algorithm,” Procedia Computer Science, vol. 116, pp. 365–372.

P. ANTWI ET AL., 2017. “Estimation of Biogas and Methane Yields in an UASB Treating Potato Starch Processing Wastewater With Backpropagation Artificial Neural Network,” Journal Bioresour Technology, vol. 228, pp. 106–115.

Pangaribuan, J. J. (2016). Mendiagnosis Penyakit Diabetes Melitus Dengan Menggunakan Metode Extreme Learning Machine. Journal Information System Development (ISD), 1(2).

TANG, Jiexiong; DENG, Chenwei; HUANG, Guang-Bin. 2015. Extreme learning machine for multilayer perceptron. IEEE transactions on neural networks and learning systems, 27.4: 809-821.




DOI: http://dx.doi.org/10.30645/j-sakti.v3i2.147

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