Saputra, Andika Jaka
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Optimization Strategy for Electric Vehicle Charging Station Development at Gas Stations Using GIS-AHP-SAW Framework Saputra, Andika Jaka; Suharjito, Suharjito
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4744

Abstract

The rapid adoption of electric vehicles (EVs) requires the acceleration of electric vehicle charging station (EVCS) development. However, selecting optimal locations for EVCS development remains challenging. The EVCS Infrastructure Standard emphasizes that technical factors are essential, which aligns with earlier studies that point out the need to consider technical requirements along with sustainability criteria. This study aims to identify a novel optimization strategy for the EVCS development at gas stations, utilizing both technical and sustainability factors. We identified the gas station as an alternative site, conforming to regulatory guidelines and prior studies. This Framework integrates GIS, AHP, and SAW methods to achieve the research objectives. We evaluated the framework using suitability analysis, mathematical optimization techniques and conducted empirical study in a designated region of Indonesia to assess the practical applicability. The study's revealed substantial findings and efficient optimization strategies. The power network subcriterion ranking as the most critical in the hierarchy of criteria. The GS05 and GS22 locations attain an optimal level across all optimization scenarios. The improved accessibility of power network facilities can augment the total alternative weight by 22.5% and improve the coverage demand from 6% to 47%. The results indicated the optimization strategy focused on improving electricity network facilities at the gas station is the best strategy for EVCS Development. This framework demonstrated a replicable model for decision support systems within the domain of spatial informatics and smart infrastructure planning, specifically spatial decision support systems for EV infrastructure planning, and offers valuable insights for investor decision-making.