Implementation of ANN-CFB Methods in Measuring Community Satisfaction Level of Denpasar City on the Aspect of Government Services
Abstract
The concept of a smart city is indeed presented as an answer for efficient management of resources. Support for applications that are constantly evolving and the creation of a creative ecosystem in the field of technology, is a good first step towards a smart city. But in reality smart city is not only related to technology. This concept is a combination of new technology and intelligent thinking about the use of technology. As a city full of allure, Denpasar City along with its development and population growth, began to emerge various problems such as decreasing the quality of public services, congestion on the road, accumulation of garbage and other social problems. To solve these problems, Denpasar needs a smart, creative and innovative solution run by the ranks of government officials, from leaders to the lowest levels, and supported by the full commitment of all its citizens. Various efforts have been made by the Denpasar City government to facilitate services to the community, ranging from building a system to facilitate services to the community such as the health service system, population service system, government service system (e-Gove), and the public complaints system. To find out whether the efforts made by the government are related to the services provided to the community by implementing a smart city system. In this study various measurements of satisfaction levels were carried out to obtain significant conclusions. Of the five aspects studied, namely aspects of government services, aspects of government transparency, aspects of health services, aspects of population service, aspects of transportation services and aspects of water supply and electricity services, in general the people of Denpasar expressed satisfaction with a level of satisfaction of 76,312 and the level of satisfaction with aspects of health services have the highest level of satisfaction that is equal to 88,574.
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[2] Narad, S. and Chavan, P. 2016. Cascade Forward Back-propagation Neural Network Based Group Authentication Using (n, n) Secret Sharing Scheme. Procedia Computer Sci. 78, pp.185-191.
[3] Rukmana, P., Aprilia, V.R., Suhartono, D. and Wongso, R. 2014. Summarizing Text for Indonesian Language by Using Latent Dirichlet Allocation and Genetic Algorithm. Proceeding of the Electrical Engineering Computer Science and Informatics, 1(1), pp.148-153.
[4] Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
[5] LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D. 1990. Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems (pp. 396-404).
[6] Zhang, Z., 2016. Derivation of Backpropagation in Convolutional Neural Network (CNN). University of Tennessee, Knoxville, TN.
[7] Vihikan, W.O., Putra, D., Gede, I.K. and Dharmaadi, I., 2017. Foreign Tourist Arrivals Forecasting Using Recurrent Neural Network Backpropagation through Time. Telkomnika, 15(3).
[8] Law, R. And Au, N. 1999. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), pp.89-97.
[9] Badde, D.S., Gupta, A.K. and Patki, V.K., 2013. Cascade and feed forward back propagation artificial neural network models for prediction of compressive strength of ready mix concrete. IOSR Journal of Mechanical and Civil Engineering, 3(1), pp.1-6.
[10] Lashkarbolooki, M., Shafipour Z. S., & Hezave A. Z. 2013. Trainable Cascade-Forward Backpropagation Network Modelling of Spearmint Oil Extraction in Packed Bed Using SC-CO2. The Journal of Supercritical Fluids Vol 73: 108 – 115.
[11] Tengeleng, Siddi & Nzeukou Armand. 2014. Performance of Using Cascade Forward Back Propagation Neural Network for Estimating Rain Parameter with Rain Drop Size Distribution. Atmosphere Mdpi Journal Vol 5: 454-472.
[12] Guiming, S. & Jidong, S., 2016. Remote Sensing Image Edge-Detection Based on Improved Canny Operator. IEEE 8th International Conference on Communication Software and Networks, pp. 652-65
