Electric vehicles (EVs) are vehicles entirely powered by electric motors using energy stored in batteries. In Indonesia, interest in electric vehicles is increasing, supported by government initiatives to reduce carbon emissions and improve infrastructure. The main issues faced are potential buyers' hesitation in choosing electric vehicles due to the limited variety of models, high prices, and insufficient information provided to buyers. This research aims to build a decision support system for selecting electric vehicles using the Simple Additive Weighting (SAW) method. The selection of electric vehicles using the SAW method requires criteria derived from sales brochures, official dealer websites, automotive exhibitions, and trusted news sources. The criteria used include price, range, battery capacity, passenger capacity, and vehicle speed. In the application development process, the waterfall method was used. The modeling tools used in this research are Flowcharts, Data Flow Diagrams, and Entity Relationship Diagrams, while the application development uses HTML and JavaScript. Based on the research conducted, all features function well, and out of the five alternatives used in this study, the results show that the Hyundai Ioniq 6 has a score of 0.9, while the Wuling Air EV Long Range has a score of 0.59.


    Access

    Download


    Export, share and cite



    Title :

    Decision Support System for Electric Vehicles Selection Using Simple Additive Weighting


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Integrating Simple Additive Weighting in Robotics Decision Support Systems

    Yanti, Dewi / Suharyanto, Agung / Firdaus, Winci et al. | Springer Verlag | 2024



    Assessment of Toll User Satisfaction in East Java Using Simple Additive Weighting (SAW)

    Pradityo Utomo / Laily Fatmawati / Rochidajah Rochidajah et al. | DOAJ | 2022

    Free access


    Risk-Averse Decision Support for Optimal Use of Electric Vehicles

    Borth, Michael / Kupper, Frank / Mulder, Lars et al. | IEEE | 2024