Neural Network Analysis With Backpropogation In Predicting Human Development Index (HDI) Component by Regency/City In North Sumatera

Muhammad Noor Hasan Siregar(1*),

(1) Universitas Graha Nusantara, Padangsidimpuan, Sumatera Utara
(*) Corresponding Author


Human Development Index (HDI) measures human development outcomes based on a number of basic components of quality of life. As a measure of the quality of life, HDI is built through a basic three-dimensional approach. Data obtained from the Central Bureau of Statistics 2015 for Human Development Index (HDI) by Regency / City in North Sumatera Province consisting of 32 alternatives and with 4 parameters ie life expectancy (year), expectation, school length (%), the average length of school (year) and per capita real expenditure (Rp). By using backpropagation obtained result of 6 testing of architecture pattern that is: 4-5-1, 4-10-1, 4-5-10-1, 4-10-5-1, 4-10-20-1 and 4- 15-20-1 obtained best architectural pattern is 4-10-20-1 with epoch 2126, error 0.0011757393, execution time 00:16 and accuracy 100%.

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Khalid, Mohd Sazali, et al. "Teaching and Learning Using Computers: How Should We Tread on Its' Changing Technology?." International Journal of Emerging Technologies in Learning 9.5 (2014).

Chiroma, H., Abdulkareem, S., Abubakar, A.I. and Herawan, T., 2014. Kernel functions for the support vector machine: comparing performances on crude oil price data. In Recent Advances on Soft Computing and Data Mining (pp. 273-281). Springer International Publishing.

Hakim, R.F., Sari, E.N. and Herawan, T., 2014. Soft Solution of Soft Set Theory for Recommendation in Decision Making. In Recent Advances on Soft Computing and Data Mining (pp. 313-324). Springer International Publishing.

Lasisi, A., Ghazali, R. and Herawan, T., 2014. Comparative performance analysis of negative selection algorithm with immune and classification algorithms. In Recent Advances on Soft Computing and Data Mining (pp. 441-452). Springer International Publishing.

Handaga, B., Herawan, T. and Deris, M.M., 2012. FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory. International Journal of Fuzzy System Applications (IJFSA), 2(4), pp.29-46.

Herawan, T., Abdullah, Z., Chiroma, H., Sari, E.N., Ghazali, R. and Nawi, N.M., 2014, September. Cauchy criterion for the Henstock-Kurzweil integrability of fuzzy number-valued functions. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 1329-1333). IEEE.

Ma, X., Qin, H., Sulaiman, N., Herawan, T. and Abawajy, J.H., 2014. The parameter reduction of the interval-valued fuzzy soft sets and its related algorithms. IEEE Transactions on Fuzzy Systems, 22(1), pp.57-71.

Yanto, I.T.R., Ismail, M.A. and Herawan, T., 2016. A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering. Engineering Applications of Artificial Intelligence, 53, pp.41-52.

Abubakar, A.I., Zeki, A., Chiroma, H. and Herawan, T., 2014. Investigating Rendering Speed and Download Rate of Three-Dimension (3D) Mobile Map Intended for Navigation Aid Using Genetic Algorithm. In Recent Advances on Soft Computing and Data Mining (pp. 261-271). Springer International Publishing.

Qin, H., Ma, X., Herawan, T. and Zain, J.M., 2012, May. An improved genetic clustering algorithm for categorical data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 100-111). Springer Berlin Heidelberg.

Shah, H., Ghazali, R., Nawi, N.M., Deris, M.M. and Herawan, T., 2013. Global artificial bee colony-Levenberq-Marquardt (GABC-LM) algorithm for classification. International Journal of Applied Evolutionary Computation (IJAEC), 4(3), pp.58-74.

Castaño, B., Moreno, Á., Carbajo, M. and de Pedro, J., 2008. Artificial Intelligence and Bluetooth Techniques in a Multi-user M-learning Domain. IJCSA, 5(1), pp.1-13.

Abubakar, A.I., Khan, A., Nawi, N.M., Rehman, M.Z., Wah, T.Y., Chiroma, H. and Herawan, T., 2016. Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms. Journal of Computational and Theoretical Nanoscience, 13(1), pp.450-460.

Chiroma, H., Abdul-kareem, S., Ibrahim, U., Ahmad, I.G., Garba, A., Abubakar, A., Hamza, M.F. and Herawan, T., 2015. Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear. Neural Network World, 25(5), p.565.

Husaini, N.A., Ghazali, R., Nawi, N.M., Ismail, L.H., Deris, M.M. and Herawan, T., 2014. Pi-Sigma Neural Network For A One-Step-Ahead Temperature Forecasting. International Journal of Computational Intelligence and Applications, 13(04), p.1450023.

Nawi, N.M., Rehman, M.Z., Aziz, M.A., Herawan, T. and Abawajy, J.H., 2014, November. Neural network training by hybrid accelerated cuckoo particle swarm optimization algorithm. In International Conference on Neural Information Processing (pp. 237-244). Springer International Publishing.

Nawi, N.M., Rehman, M.Z., Aziz, M.A., Herawan, T. and Abawajy, J.H., 2014, November. An Accelerated Particle Swarm Optimization Based Levenberg Marquardt Back Propagation Algorithm. In International Conference on Neural Information Processing (pp. 245-253). Springer International Publishing.

Chiroma, H., Abdul-Kareem, S., Muaz, S.A., Khan, A., Sari, E.N. and Herawan, T., 2014, October. Neural Network Intelligent Learning Algorithm for Inter-related Energy Products Applications. In International Conference in Swarm Intelligence (pp. 284-293). Springer International Publishing.

Che, Z.G., Chiang, T.A. and Che, Z.H., 2011. Feed-forward neural networks training: A comparison between genetic algorithm and back-propagation learning algorithm. International Journal of Innovative Computing, Information and Control, 7(10), pp.5839-5850.

Choudhary, N.Y., Patil, M.R., Bhadade, U. and Chaudhari, B.M., 2013. Signature Recognition & Verification System Using Back Propagation Neural Network. International Jorunal of IT, Engineering and Applied Sciences Research (IJIEASR), 2(1), pp.1-8.

Baboo, S.S. and Shereef, I.K., 2010. An efficient weather forecasting system using artificial neural network. International journal of environmental science and development, 1(4), p.321.

Sumijan, A. P. Windarto, A. Muhammad, and Budiharjo, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,” Int. J. Softw. Eng. Its Appl., vol. 10, no. 10, pp. 189–204, 2016.



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