Hybrid School Selection System Using R-Tree Spatial Index Algorithm and SAW Method

• Published in: 2023 1st International Conference on Advanced Engineering and Technologies (ICONNIC) pp. 362 - 367
• Main Authors: Sunaryono, Dwi, Purwananto, Yudhi, Sabilla, Irzal Ahmad, Wijaya, William Handi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.10.2023
• Subjects: 2022 East Java new students admission; Additives; Aging; Java; multi-criteria decision method (MCDM); Performance evaluation; r-tree algorithm; Real-time systems; simple additive weighting (SAW); spatial index; Time factors; Urban areas
• DOI: 10.1109/ICONNIC59854.2023.10467541

In 2022 East Java new students admission, the verifier school and recommended schools determination system can still be improved because some students still get a school that is either far from their residence or has a long verification queue. Therefore, the purpose of the proposed research is to design a hybrid school selection system using R-Tree spatial index algorithm and Simple Additive Weighting (SAW) method in the case study of 2022 East Java new students admission. This research is also proposed to fill the gap in performance comparison between system that use MCDM method with and without spatial index. The performance of the spatial index system, non spatial index system, and the old system used in 2022 East Java new students admission will be compared using random sample from students dataset in 2022 East Java new students admission. After performance evaluation, it can be concluded that the spatial index system has an accuracy of 99.96% when compared to the system without spatial index. Additionally, in terms of time, the spatial index system also shows a performance improvement of 57.56% compared to the system without spatial index. Furthermore, the system employing spatial indexing and the Simple Additive Weighting (SAW) method also surpasses the legacy system by 1.28% in terms of decision quality (accuracy).