International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 15, No. 1, 8 Feb. 2023
Cover page and Table of Contents: PDF (size: 418KB)
A Model Statistical during Covid-19 Future E-Commerce Revenue for Indonesia Aviation
Full Text (PDF, 418KB), PP.51-57
Views: 0 Downloads: 0
Author(s)
Taufik Hidayat
1,*
Rahutomo Mahardiko
2
Ali Miftakhu Rosyad
3
Index Terms
Aviation industry, Covid-19, Future prediction, Neural network, Statistical model
Abstract
Until today, Information Technology (IT) has been felt by aviation industry showed by positive growth of operating revenue before Covid-19 pandemic. The pandemic of Covid-19 changes the world especially the aviation industry by slowing down the business transaction. This study presents statistical model on recent e-commerce revenue of aviation, the number of passengers and the IT investments then predicts future of e-commerce revenue, the number of passengers and the IT spending using Neural Networks. This method is useful to predict the future because it follows the time being. The chosen variables are intended whether IT has an impact during the pandemic for passenger generation year by year. The results show that for the next few years, the revenue, the number of passengers and the IT spending are significantly increasing, while there are problems faced in aviation industry because of Covid-19. This model also can be applied for other industry.
Cite This Paper
Taufik Hidayat, Rahutomo Mahardiko, Ali Miftakhu Rosyad, "A Model Statistical during Covid-19 Future E-Commerce Revenue for Indonesia Aviation", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.15, No.1, pp. 51-57, 2023. DOI:10.5815/ijieeb.2023.01.05
Reference
[1]B. Moriset, E-business and e-commerce, International Encyclopedia of Human Geography (Second Edition), pp. 1–10, 2020. https://doi.org/10.1016/B978-0-08-102295-5.10044-7
[2]R. Mahardiko, IT value analysis: Case study of PT. express transindo utama, In Proceedings 2014 International Conference on Information Technology Systems and Innovation (ICITSI), 2014, pp. 224–229. https://doi.org/10.1109/ICITSI.2014.7048268
[3]C. M. Gheorghe, and C. Stoenescu, Information technology - A tool to redefine the air travel essentials, Romanian Economic and Business Review, vol. 10, no. 2, pp. 155–166, 2015. http://www.rebe.rau.ro/RePEc/rau/journl/SU15/REBE-SU15-A14.pdf
[4]D. G. Cockayne, Sharing and neoliberal discourse: The economic function of sharing in the digital on-demand economy, Geoforum, vol. 77, pp. 73–82, 2016. https://doi.org/10.1016/j.geoforum.2016.10.005
[5]P. Adler, R. Florida, K. King, and C. Mellander, The city and high-tech startups: The spatial organization of Schumpeterian entrepreneurship, Cities, vol. 87, pp. 121–130, 2019. https://doi.org/10.1016/j.cities.2018.12.013
[6]P. Niewiadomski, Global production networks in the passenger aviation industry, Geoforum, vol. 87, pp. 1–14, 2017. https://doi.org/10.1016/j.geoforum.2017.09.013
[7]Ö. Peksatici, and H. S. Ergun, The gap between academy and industry - A qualitative study in Turkish aviation context, Journal of Air Transport Management, vol. 79, 2019. https://doi.org/10.1016/j.jairtraman.2019.101687
[8]B. A. Koroglu, and T. O. Eceral, Human capital and innovation capacity of firms in defense and aviation industry in Ankara, Procedia - Social and Behavioral Sciences, vol. 195, pp. 1583–1592, 2015. https://doi.org/10.1016/j.sbspro.2015.06.196
[9]A. Jasari, Information systems at the airport, In Proceedings International Conference on Economic and Social Studies (ICESoS), 2015, pp. 8–13. https://icesos.ibu.edu.ba/wp-content/uploads/2019/02/PROCEEDING-BOOK_V41.10.2015-min.pdf
[10]R. A. Price, C. De Lille, and K. Bergema, Advancing industry through design: A longitudinal case study of the aviation industry, She Ji: The Journal of Design, Economics, and Innovation, vol. 5, no. 4, pp. 304–326, 2019. https://doi.org/10.1016/j.sheji.2019.07.003
[11]G. Büyüközkan, C. A. Havle, and O. Feyzioğlu, A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP, Journal of Air Transport Management, vol. 86, 2020. https://doi.org/10.1016/j.jairtraman.2020.101817
[12]AirlineRatings, Garuda Indonesia - Full service carrier, 2020. https://www.airlineratings.com/ratings/garuda-indonesia
[13]M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis, M. Agha, and R. Agha, The socio-economic implications of the coronavirus pandemic (COVID-19): A review, International Journal of Surgery, vol. 78, pp. 185–193, 2020. https://doi.org/10.1016/j.ijsu.2020.04.018
[14]S. Gössling, D. Scott, and C. M. Hall, Pandemics, tourism and global change: A rapid assessment of COVID-19, Journal of Sustainable Tourism, pp. 1–20, 2020. https://doi.org/10.1080/09669582.2020.1758708
[15]J. Sarkis, M. J. Cohen, P. Dewick, and P. Schröder, A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production, Resources, Conservation and Recycling, vol. 159, 2020. https://doi.org/10.1016/j.resconrec.2020.104894
[16]S. Wellman, J. Moskal, C. L. Barnes, and J. Parvizi, Business unusual: COVID-19 ramifications for arthroplasty, The Journal of Arthroplasty, vol. 35, pp. S1–S2, 2020. https://doi.org/10.1016/j.arth.2020.04.062
[17]J. A. Hardie, and P. A. Brennan, Are you surgically current? Lessons from aviation for returning to non-urgent surgery following COVID-19, British Journal of Oral and Maxillofacial Surgery, 2020. https://doi.org/10.1016/j.bjoms.2020.06.012
[18]E. Kasturi, S. Prasanna Devi, S. Vinu Kiran, and S. Manivannan, Airline route proifatilibity analysis and optimization using big data analyticson aviation data sets under heuristic techniques, Procedia Computer Science, vol. 87, pp. 86–92, 2016. https://doi.org/10.1016/j.procs.2016.05.131
[19]R. de A. Araújo, N. Nedjah, A. L. I. Oliveira, and S. R. de L. Meira, A deep increasing–decreasing-linear neural network for financial time series prediction, Neurocomputing, vol. 347, pp. 59–81, 2019. https://doi.org/10.1016/j.neucom.2019.03.017
[20]M. B. Wagena, D. Goering, A. S. Collick, E. Bock, D. R. Fuka, A. Buda, and Z. M. Easton, Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models, Environmental Modelling & Software, vol. 126, 2020. https://doi.org/10.1016/j.envsoft.2020.104669
[21]B. K. Iwana, V. Frinken, and S. Uchida, DTW-NN: A novel neural network for time series recognition using dynamic alignment between inputs and weights, Knowledge-Based Systems, vol. 188, 2020. https://doi.org/10.1016/j.knosys.2019.104971
[22]F. Ambrosino, C. Sabbarese, V. Roca, F. Gludicepietro, and G. Chiodini, Analysis of 7-years Radon time series at Campi Flegrei area (Naples, Italy) using artificial neural network method, Applied Radiation and Isotopes, vol. 163, 2020. https://doi.org/10.1016/j.apradiso.2020.109239
[23]C. Zhang, H. Tang, and Z. Duan, Time series analysis of volleyball spiking posture based on quality-guided cyclic neural network, Journal of Visual Communication and Image Representation, 2019. https://doi.org/10.1016/j.jvcir.2019.102681
[24]T. Hidayat, R. Mahardiko, and M. Alaydrus, Mobile cellular technology forecast for the Indonesian telecommunications industry, Journal of Telecommunications and the Digital Economy, vol. 8, no. 1, pp. 37–48, 2020. https://doi.org/10.18080/jtde.v8n1.226
[25]X. Wei, L. Zhang, H. -Q. Yang, L. Zhang, and Y. -P. Yao, Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks, Geoscience Frontiers, 2020. https://doi.org/10.1016/j.gsf.2020.04.011
[26]T. Hidayat, and M. Alaydrus, Performance analysis and mitigation of virtual machine server by using Naive Bayes classification, In Proceedings 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019, pp. 1–5. https://doi.org/10.1109/ICIC47613.2019.8985932
[27]T. Hidayat, and R. Mahardiko, Mathematical model to forecast future banking income, International Journal Mathematics in Operational Research, In Press. https://doi.org/10.1504/IJMOR.2020.10032545
[28]T. Hidayat, D. S. T. Franky, and R. Mahardiko, forecast analysis of research chance on AES algorithm to encrypt during data transmission on cloud computing, In Proceedings 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP), 2020, pp. 163–166. https://doi.org/10.1109/BCWSP50066.2020.9249478
[29]L. Abdurrahman, Hybrid configuration in information technology value model. IEEE Systems Journal (Early Access), pp. 1–8, 2020. https://doi.org/10.1109/JSYST.2020.2968415
[30]R. A. Susilo, and Suhardi, IT value analysis by resource-based view theory: The case study of PT. PLN (Persero) Indonesia, in Proceedings International Conference on Information Technology Systems and Innovation, 2014, pp. 213–218. https://doi.org/10.1109/ICITSI.2014.7048266
[31][Suhardi, N. B. Kurniawan, A. Subrata, and J. Sembiring, modeling it value based on meta-analysis, in Proceedings International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 330–335. https://doi.org/10.1109/EECSI.2017.8239133
[32]I. E. Patola, Suhardi, and N. B. Kurniawan, Modeling IT public value for IT performance measurement in government organization: A case study: BPS-statistics Indonesia, in Proceedings 2016 International Conference on Information Technology Systems and Innovation (ICITSI), 2016, pp. 1–6. https://doi.org/10.1109/ICITSI.2016.7858194
[33]T. Hidayat, and R. Mahardiko, Model Development of Information Technology Value for Downstream Petroleum Industry, in Proceedings 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2020, pp. 609–614. https://doi.org/10.1109/ISRITI51436.2020.9315369
[34]T. Hidayat, and R. Mahardiko, Validation of Information Technology Value Model for Petroleum Industry, in Proceedings 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2020, pp. 615–620. https://doi.org/10.1109/ISRITI51436.2020.9315382