Evaluation of MVNO model implementation in remote and border areas using the consistent fuzzy preference relations method
Main Article Content
Abstract
Law No. 36 of 1999 concerning Telecommunication has brought many changes, especially in the development of telecommunications infrastructure in Indonesia. However, the penetration of telecommunications services in the forefront, outermost, and backward regions is still relatively low. The government has made various efforts in terms of minimizing the gap in telecommunication services between urban and rural areas through various programs. However, an acceleration is needed so that the service disparity can be immediately overcome. One of the telecommunications products that can be applied to overcome these barriers is the Mobile Virtual Network Operator (MVNO). This study evaluates the most appropriate type of MVNO model to be applied in Indonesia by implementing the Consistent Fuzzy Preference Relations (CFPR) method. This method is able to accommodate expert opinion through a series of scientific steps so as to produce weights for each alternative type of MVNO model. The results obtained are that the most appropriate model to be applied in Indonesia by taking into account the criteria given. The implementation of this model is expected to be able to encourage the optimization of BTS USO that has been declared by the government.
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How to Cite
[1]
A. Isnawati, R. Pandiya, and A. Wahyudin, “Evaluation of MVNO model implementation in remote and border areas using the consistent fuzzy preference relations method”, INFOTEL, vol. 13, no. 4, pp. 223-229, Dec. 2021.
Section
Telecommunication
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References
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[2] Peraturan Pemerintah Republik Indonesia Nomor 7 Tahun 2009 tentang Jenis dan Tarif atas Jenis Penerimaan Negara Bukan Pajak yang Berlaku pada Departemen Komunikasi dan Informatika
[3] S. Lee, S. M. Chan-Olmsted, and H.-H. Ho, “The Emergence of Mobile Virtual Network Operators (MVNOs): An Examination of the Business Strategy in the Global MVNO Market,” Int. J. Media Manag., vol. 10, no. 1, pp. 10–21, 2008, doi: 10.1080/14241270701820424.
[4] J. Do Song, “Various wholesale price equilibria for mobile virtual network operators,” Telecomm. Policy, vol. 34, no. 10, pp. 633–648, 2010, doi: 10.1016/j.telpol.2010.07.002.
[5] B. W. Kim and S. H. Seol, “Economic analysis of the introduction of the MVNO system and its major implications for optimal policy decisions in Korea,” Telecomm. Policy, vol. 31, no. 5, pp. 290–304, 2007, doi: 10.1016/j.telpol.2007.03.002.
[6] J. Bustillo, “Digital services in the 21st century: a strategic and business perspective [book review],” in Digital service in the 21st century: A strategic and business perspective, John Wiley & Sons, Inc, 2017, pp. 155–162.
[7] P. Kalmus and L. Wiethaus, “On the competitive effects of mobile virtual network operators,” Telecomm. Policy, vol. 34, no. 5–6, pp. 262–269, 2010, doi: 10.1016/j.telpol.2010.04.002.
[8] X. Xiao, R. Zhang, J. Wang, C. Qiao, and K. Lu, “An optimal pricing scheme to improve transmission opportunities for a mobile virtual network operator,” Comput. Networks, vol. 99, pp. 51–65, 2016, doi: 10.1016/j.comnet.2016.02.004.
[9] T. Zhang, H. Wu, X. Liu, and L. Huang, “Learning-aided scheduling for mobile virtual network operators with QoS constraints,” 2016 14th Int. Symp. Model. Optim. Mobile, Ad Hoc, Wirel. Networks, WiOpt 2016, 2016, doi: 10.1109/WIOPT.2016.7492913.
[10] S. M. Gaikwad, P. Mulay, and R. R. Joshi, “Analytical hierarchy process to recommend an ice cream to a diabetic patient based on sugar content in it,” Procedia Comput. Sci., vol. 50, pp. 64–72, 2015, doi: 10.1016/j.procs.2015.04.062.
[11] E. W. Stein, “A comprehensive multi-criteria model to rank electric energy production technologies,” Renew. Sustain. Energy Rev., vol. 22, pp. 640–654, 2013, doi: 10.1016/j.rser.2013.02.001.
[12] M. Kamali, A. A. Alesheikh, Z. Khodaparast, S. M. Hosseinniakani, and S. A. Alavi Borazjani, “Application of delphi-AHP and fuzzy-GIS approaches for site selection of large extractive industrial units in Iran,” J. Settlements Spat. Plan., vol. 6, no. 1, pp. 9–17, 2015.
[13] L. A. Vidal, F. Marle, and J. C. Bocquet, “Measuring project complexity using the Analytic Hierarchy Process,” Int. J. Proj. Manag., vol. 29, no. 6, pp. 718–727, 2011, doi: 10.1016/j.ijproman.2010.07.005.
[14] J. Franek and A. Kresta, “Judgment Scales and Consistency Measure in AHP,” Procedia Econ. Financ., vol. 12, no. December, pp. 164–173, 2014, doi: 10.1016/s2212-5671(14)00332-3.
[15] A. Jafarnejad, M. Ebrahimi, M. A. Abbaszadeh, and S. A. Abtahi, “Risk Management in Supply Chain using Consistent Fuzzy Preference Relations,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 4, no. 1, pp. 77–89, 2014, doi: 10.6007/IJARBSS/v4-i1/514.
[16] R. J. Chao and Y. H. Chen, “Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations,” Expert Syst. Appl., vol. 36, no. 7, pp. 10657–10662, 2009, doi: 10.1016/j.eswa.2009.02.047.
[17] B. Zhu and Z. Xu, “A fuzzy linear programming method for group decision making with additive reciprocal fuzzy preference relations,” Fuzzy Sets Syst., vol. 246, pp. 19–33, 2014, doi: 10.1016/j.fss.2014.01.001.
[18] E. K. Aydogan, O. Demirtas, and M. Dagdeviren, “A New Integrated Fuzzy Multi-Criteria Decision Model for Performance Evaluation,” Bus. Manag. Stud., vol. 1, no. 1, p. 38, 2015, doi: 10.11114/bms.v1i1.704.
[19] S. Verma and S. Chaudhri, “Integration of Fuzzy Reasoning approach (FRA) and Fuzzy Analytic Hierarchy Process (FAHP) for Risk Assessment in Mining Industry,” J. Ind. Eng. Manag., vol. 2, no. 1, pp. 9–15, 2013, doi: http://dx.doi.org/10.3926/jiem.948.
[20] M. Shaverdi, M. R. Heshmati, and I. Ramezani, “Application of fuzzy AHP approach for financial performance evaluation of iranian petrochemical sector,” Procedia Comput. Sci., vol. 31, no. Itqm, pp. 995–1004, 2014, doi: 10.1016/j.procs.2014.05.352.
[21] R. P. Kusumawardani and M. Agintiara, “Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process,” Procedia Comput. Sci., vol. 72, pp. 638–646, 2015, doi: 10.1016/j.procs.2015.12.173.
[22] Z. Turskis, E. K. Zavadskas, J. Antucheviciene, and N. Kosareva, “A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection,” Int. J. Comput. Commun. Control, vol. 10, no. 6, 2015, doi: 10.15837/ijccc.2015.6.2078.