Rancang Bangun Sistem Informasi Rental Kendaraan dengan Fitur Pencarian Optimal menggunakan Metode Simple Additive Weighting (SAW) - Muhammad Ibnu Alif; Heri Supriyanto; Arif Zeinfiki Djunaedi (STIKes Ibnu Sina Ajibarang, 2026)
The rapid advancement of information technology has accelerated digital transformation across various industries, including vehicle rental services. Nevertheless, many rental companies still rely on manual administrative processes, resulting in service delays, data management errors, and difficulties for customers in selecting vehicles that meet their requirements. This study aims to design and develop a web-based vehicle rental information system equipped with an optimal vehicle recommendation feature using the Simple Additive Weighting (SAW) method. The system was developed using the Waterfall software development model, consisting of requirements analysis, system design using UML and Entity Relationship Diagrams (ERD), implementation with the Laravel framework and MySQL database, and system validation through Black Box Testing. The SAW method was employed to rank vehicle alternatives based on five decision criteria, namely rental price and fuel type as cost criteria, while passenger capacity, vehicle production year, and transmission type were treated as benefit criteria. The evaluation involving 20 vehicle alternatives indicated that Isuzu Elf (A8) achieved the highest preference value of 0.8596 (SAW score 85.96), followed by Isuzu Elf (A7) with 81.14 and Toyota Hiace (A14) with 78.42, making them the most recommended vehicles according to the assigned criterion weights. Furthermore, Black Box Testing demonstrated that all system functionalities, including user registration, authentication, vehicle recommendation, booking management, data administration, and reporting, operated successfully according to the specified functional requirements without any functional errors. The developed system effectively improves operational efficiency while providing customers with objective, accurate, and faster vehicle recommendations based on multiple decision criteria.