Determination of Industrial Location Using the WASPAS Method with Spatial Data as Criteria Data
Penentuan Lokasi Industri Menggunakan Metode WASPAS Dengan Data Spasial Sebagai Data Kriteria
Keywords:
waspas, location, industry, spearman rank
Abstract
Today, the development of the use of spatial data is not only used for information geographic or transportation. But also can be used for site selection with integrating decision support system methods. Generated information can help in making decisions and meet the expected aspects. One method that can be used to support the decision making process is the Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is included in Multi Criteria Decision Making which can produce selected information from the data or criteria used. This study uses the WASPAS method as a determinant of strategic industrial locations by spatial data collection. In determining strategic industrial locations, WASPAS uses several different criteria and weights for each criterion. The WASPAS method can produce precise information related to the determination of strategic industrial locations. The results of the Spearman Rating trial with data on industrial locations in the city of Semarang show a strong conformity, as seen from the resulting compatibility value of 1.0. The results obtained from this study are the establishment of a system model that supports the decision to determine the location of the industry using the WASPAS method.
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References
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[3] Z. Turskis, N. Goranin, A. Nurusheva, and S. Boranbayev, “A Fuzzy WASPAS-Based Approach to Determine Critical Information Infrastructures of EU Sustainable Development,” Sustainability, vol. 11, no. 2, p. 424, Jan. 2019, doi: 10.3390/su11020424.
[4] A. R. Mishra, P. Rani, K. R. Pardasani, and A. Mardani, “A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures,” Journal of Cleaner Production, vol. 238, p. 117901, Nov. 2019, doi: 10.1016/j.jclepro.2019.117901.
[5] S. Gupta, U. Soni, and G. Kumar, “Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry,” Computers & Industrial Engineering, vol. 136, pp. 663–680, Oct. 2019, doi: 10.1016/j.cie.2019.07.038.
[6] G. Stojić, Ž. Stević, J. Antuchevičienė, D. Pamučar, and M. Vasiljević, “A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products,” Information, vol. 9, no. 5, p. 121, May 2018, doi: 10.3390/info9050121.
[7] D. E. Ighravwe and S. A. Oke, “A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria,” Journal of Building Engineering, vol. 24, p. 100753, Jul. 2019, doi: 10.1016/j.jobe.2019.100753.
[8] K. A. Alam, R. Ahmed, F. S. Butt, S.-G. Kim, and K.-M. Ko, “An Uncertainty-aware Integrated Fuzzy AHP-WASPAS Model to Evaluate Public Cloud Computing Services,” Procedia Computer Science, vol. 130, pp. 504–509, 2018, doi: 10.1016/j.procs.2018.04.068.
[9] D. Pamucar, K. Chatterjee, and E. K. Zavadskas, “Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers,” Computers & Industrial Engineering, vol. 127, pp. 383–407, Jan. 2019, doi: 10.1016/j.cie.2018.10.023.
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[19] M. PU, “Pedoman Kriteria Teknis Kawasan Budi Daya.” Kementrian Pekerjaan Umum, 2007.
Published
2020-08-17
How to Cite
Agusta Praba Ristadi Pinem, Asmiatun, S., & Putri, A. N. (2020). Determination of Industrial Location Using the WASPAS Method with Spatial Data as Criteria Data. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 691 - 696. https://doi.org/10.29207/resti.v4i4.2094
Section
Information Systems Engineering Articles
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