Application of MCDM-AHP and EDAS Methods for Selection of the Best Residential Locations Areas
DOI:
10.33395/sinkron.v8i4.13661Keywords:
EDAS, MCDM-AHP, Multi-criteria, Residential location area, SelectionAbstract
The population density has led to an expansion of the area where people live. This opportunity is exploited by housing developers to build many locations for the development of residential areas. The purpose of writing this paper is to provide proper consideration in housing selection which can be seen from various parameters as selection criteria. The method support that can be used in residential selection is the collaboration of the MCDM-AHP and EDAS methods. This method can be used as a recommendation against the concept of multi-criteria. The more criteria used, the higher the level of difficulty to support decision making. With the collaboration of the MDCM-AHP method, it can be used to provide an assessment of multi-criteria that have optimal values, while the EDAS method will be used as a strength in evaluating the selection of alternatives based on positive and negative distances for different types of criteria through normalized values. Determination of the weighting value of the criteria is obtained through the iteration stages using the mathematical algebra matrices method and proven by expert choice apps. The decision support results obtained provide a ranking value with the first priority being PR06 with an accumulative weight of 0.552 followed by the second and third ranks respectively PR04 and PR05 with a weight of 0.545 and 0.522 respectively. Thus supporting decision making with the recommendation of the MCDM-AHP and EDAS method collaboration can provide an optimal assessment of residential selection in a detailed and accurate manner.
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Akmaludin, A., Hartati, T., Purwanto, H., & Sukendar, T. (2020). The Best Selection of Programmers in Generation 4 . 0 Using AHP and ELECTRE Elimination Methods The Best Selection of Programmers in Generation 4 . 0 Using AHP and ELECTRE Elimination Methods. Journal Conference Series, 0–7. https://doi.org/10.1088/1742-6596/1477/3/032001
Akmaludin, Cahyadi, C., Kuswanto, H., Rahman, T., Sudradjatand, A., & Panca, E. (2020). Research Article Decision Support For Selection of System Analyst in Industry 4.0 Generation Era Using: MCDM-AHP And Prometee Elimination Methods. 2010.
Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), 445–459. https://doi.org/10.1142/S0218488506004114
Chinram, R., Hussain, A., Mahmood, T., & Ali, M. I. (2021). EDAS method for multi-criteria group decision making based on intuitionistic fuzzy rough aggregation operators. IEEE Access, 9, 10199–10216. https://doi.org/10.1109/ACCESS.2021.3049605
Elboshy, B., Alwetaishi, M., M. H. Aly, R., & Zalhaf, A. S. (2022). A suitability mapping for the PV solar farms in Egypt based on GIS-AHP to optimize multi-criteria feasibility. Ain Shams Engineering Journal, 13(3), 101618. https://doi.org/10.1016/j.asej.2021.10.013
Fan, J. P., Li, Y. J., & Wu, M. Q. (2019). Technology Selection Based on EDAS Cross-Efficiency Evaluation Method. IEEE Access, 7, 58974–58980. https://doi.org/10.1109/ACCESS.2019.2915345
Farkas, A. (2007). The analysis of the principal eigenvector of pairwise comparison matrices. Acta Polytechnica Hungarica, 4(2), 1–17.
Ganshina, E. Y., & Smirnova, I. L. (2022). Analysis of the World Fleet Capacities in the Context of the Northern Sea Route with Help of the EDAS Methodology. Transportation Research Procedia, 61, 266–272. https://doi.org/10.1016/j.trpro.2022.01.044
Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica (Netherlands), 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Haghighi, S. (2012). Application of Analytical Hierarchy Process ( AHP ) Technique To Evaluate and Selecting Suppliers in an Effective Supply Chain. 1(8), 1–14.
Ishizaka, A., & Labib, A. (2009). Analytic Hierarchy Process and Expert Choice: Benefits and limitations. OR Insight, 22(4), 201–220. https://doi.org/10.1057/ori.2009.10
Jihadi, M., Vilantika, E., Sholichah, F., & Arifin, Z. (2021). Best Sharia Bank In Indonesia: An Analytical Hierarchy Process (Ahp) Approach. International Journal of the Analytic Hierarchy Process, 13(1), 92–106. https://doi.org/10.13033/ijahp.v13i1.824
Kauko, T. (2003). Residential property value and locational externalities: On the complementarity and substitutability of approaches. Journal of Property Investment & Finance, 21(3), 250–270. https://doi.org/10.1108/14635780310481676
Mari, M. (2021). The importance of location factors in determining land prices: The evidence from Bratislava’s hinterland. Region, 8(1), 181–198. https://doi.org/10.18335/region.v8i1.328
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. https://doi.org/10.1504/IJSSCI.2008.017590
Saaty, T. L. (2010). The Eigenvector In Lay Language 2 . What we learn when we have measurement. 2(2), 163–169.
Schitea, D., Deveci, M., Iordache, M., Bilgili, K., Akyurt, İ. Z., & Iordache, I. (2019). Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS. International Journal of Hydrogen Energy, 44(16), 8585–8600. https://doi.org/10.1016/j.ijhydene.2019.02.011
Srivastava, P., Mustafa, A., Khanduja, D., Chowdhary, S. K., Kumar, N., Kartik, & Shukla, R. K. (2020). Prioritizing Autonomous Maintenance System Attributes using Fuzzy EDAS Approach. Procedia Computer Science, 167(2019), 1941–1949. https://doi.org/10.1016/j.procs.2020.03.217
Waas, D. V., Sudipa, I. G. I., Agus, I. P., & Darma, E. (2022). Comparison of Final Results Using Combination AHP-VIKOR And AHP-SAW Methods In Performance Assessment ( Case Imanuel Lurang Congregation ). International Journal of Information & Technology, 5(158), 612–623.
Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. D., & Otaghsara, S. K. (2020). Evaluation of renewable energy resources using integrated Shannon Entropy—EDAS model. Sustainable Operations and Computers, 1(December), 35–42. https://doi.org/10.1016/j.susoc.2020.12.002
Zhang, S., Wei, G., Gao, H., Wei, C., & Wei, Y. (2019). Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its. Technological and Economic Development of Economy, 25(6), 1123–1138.
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