Analisis Pendekatan MCDM Berbasis Surrogate Weighting Procedures dan TOPSIS untuk Rekomendasi Motor Listrik
DOI:
https://doi.org/10.30865/json.v7i4.9734Keywords:
Sistem Pendukung Keputusan, Motor Listrik, MCDM, TOPSIS, Surrogate Weighting ProceduresAbstract
Jumlah kendaraan listrik yang semakin meningkat di pasaran dengan spesifikasi yang beragam membuat konsumen kesulitan untuk menentukan pilihan yang tepat berdasarkan kebutuhan. Penelitian ini mengembangkan sistem pendukung keputusan untuk merekomendasikan pemilihan motor listrik roda dua di Indonesia menggunakan pendekatan Multi-Criteria Decision Making (MCDM) dengan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Sistem ini mengevaluasi 30 alternatif motor listrik berdasarkan lima kriteria: harga, jarak tempuh, waktu pengisian, kapasitas baterai, dan daya maksimum motor. Penelitian ini menerapkan Surrogate Weighting Procedures yang terdiri dari Equal Weight (EW), Rank Sum (RS), Rank Reciprocal (RR), dan Rank Order Centroid (ROC) untuk menentukan bobot dan TOPSIS untuk peringkat. Hasil penelitian menunjukkan bahwa variasi metode pembobotan menghasilkan perubahan distribusi bobot yang memengaruhi nilai preferensi dan posisi peringkat alternatif. Penelitian ini memberikan kontribusi melalui sistem evaluasi motor listrik yang mampu menghasilkan rekomendasi objektif dan stabil bagi konsumen dengan menguji berbagai skenario kepentingan kriteria. Charged Rimau dan Charged Anoa menunjukkan tingkat konsistensi tertinggi karena memiliki kombinasi spesifikasi yang relatif seimbang pada seluruh kriteria, khususnya jarak tempuh tinggi, waktu pengisian yang lebih singkat, kapasitas baterai besar, serta harga yang masih kompetitif dibandingkan alternatif lain. Sementara itu, Polytron Fox-R juga menunjukkan kestabilan peringkat karena menawarkan keseimbangan antara harga, jarak tempuh, dan kapasitas baterai tanpa memiliki nilai ekstrem pada kriteria tertentu.
References
[1] Q. Wei and C. Zhou, “A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China,” Environmental Science and Pollution Research, vol. 30, no. 4, pp. 10540–10559, Jan. 2023, doi: 10.1007/S11356-022-22783-6/TABLES/11.
[2] S. Boskovic, L. Svadlenka, S. Jovcic, M. Dobrodolac, V. Simic, and N. Bacanin, “An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) - A Case Study of the Electric Vehicle Selection Problem,” IEEE Access, vol. 11, pp. 39496–39507, 2023, doi: 10.1109/ACCESS.2023.3265818.
[3] M. F. Alrifaie, A. Habbal, and B. S. Kim, “A Fuzzy-Multi Attribute Decision Making Scheme for Efficient User-Centric EV Charging Station Selection,” IEEE Access, vol. 12, pp. 161134–161154, 2024, doi: 10.1109/ACCESS.2024.3487839.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jurnal Sistem Komputer dan Informatika (JSON)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
