Penerapan Metode Multi-Attributive Border Approximation Area Comparison Dalam Rekomendasi Pemilihan Mobil Second
Abstract
Used cars or often referred to as second cars, are a popular choice for many consumers because they offer more affordable value compared to new cars. Used vehicles often have lower prices, which makes them more accessible to many people. The application of the MABAC method in used car selection recommendations helps consumers to make more informed and informed decisions. By using MABAC, important factors such as physical condition, price, vehicle age, completeness of documents, and fuel efficiency. This method allows buyers to evaluate used cars based on their personal preferences and needs, resulting in recommendations that better fit the desired criteria. The results of the secong car selection recommendation are Alternative 2 with a final value of 0.921 getting rank 1, then Alternative 3 with a final value of 0.821 getting rank 2, and Alternative 4 with a final value of 0.621 getting rank 3.
References
[2] H. Shi, L. Huang, K. Li, X.-H. Wang, and H.-C. Liu, “An extended multi-attributive Border Approximation Area comparison method for emergency decision making with complex linguistic information,” Mathematics, vol. 10, no. 19, p. 3437, 2022, doi: 10.3390/math10193437.
[3] Z. Zulkarnain and Y. Hasan, “Sistem Pendukung Keputusan Pemilihan Peserta FLS2N SMAN 1 Perbaungan Menggunakan Metode MABAC,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 1, pp. 1–7, 2021.
[4] H. C. Sonar and S. D. Kulkarni, “An integrated AHP-MABAC approach for electric vehicle selection,” Res. Transp. Bus. Manag., vol. 41, p. 100665, 2021.
[5] M. Mathew, R. K. Chakrabortty, M. J. Ryan, M. F. Ljaz, and S. A. R. Khan, “The Multi-Attributive Border Approximation Area Comparison (Mabac) Method for Decision Making under Interval-Valued Fermatean Fuzzy Environment for Green Supplier Selection,” 2021.
[6] J. Wang, G. Wei, C. Wei, and Y. Wei, “MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment,” Def. Technol., vol. 16, no. 1, pp. 208–216, 2020.
[7] A. Ahyuna, B. Rahman, F. Nugroho, I. W. S. Nirawana, and A. Karim, “Analisa Penerapan Metode MABAC dengan Pembobotan Entropy dalam Penilaian Kinerja Dosen di Era Society 5.0,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 29–39, 2023.
[8] P. Wang, J. Wang, G. Wei, C. Wei, and Y. Wei, “The multi-attributive border approximation area comparison (MABAC) for multiple attribute group decision making under 2-tuple linguistic neutrosophic environment,” Informatica, vol. 30, no. 4, pp. 799–818, 2019.
[9] Ž. Jokić, D. Božanić, and D. Pamučar, “Selection of fire position of mortar units using LBWA and Fuzzy MABAC model,” Oper. Res. Eng. Sci. Theory Appl., vol. 4, no. 1 SE-Articles, pp. 115–135, Mar. 2021, doi: 10.31181/oresta20401156j.
[10] S. R. Purba, “Sistem Pendukung Keputusan Pemilihan Dokter Terbaik di Dinas Kesehatan Kab. Simalungun Menggunakan Metode MABAC,” Pelita Inform. Inf. dan Inform., vol. 9, no. 2, pp. 129–135, 2020.
Copyright (c) 2024 Sandi Badiwibowo Atim
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.