Web-Based Decision Support System for Recommending Social Assistance Recipients using the Simple Additive Weighting Method: A Case Study of RW 08 Meruyung, Depok

Social assistance programs aim to reduce poverty, yet targeting errors often arise when recipient data are outdated and selection is carried out manually and subjectively. This study develops a web-based decision support system (DSS) that recommends prospective social assistance recipients at the neighborhood level using the Simple Additive Weighting (SAW) method. The case study is RW 08, Meruyung Village, Depok, Indonesia. Ten household criteria, collected through questionnaires and interviews, are converted into ordinal scores and aggregated with equal weights. The system was built with Laravel and MySQL following the Agile Scrum framework and supports four roles: administrator, neighborhood head (RT), community head (RW), and resident. Residents can also submit and track complaints about assistance. A worked example with three anonymized households shows how normalization and the weighted sum produce a ranking, following the same rules as the ranking module. Black-box testing of 29 test cases, covering authentication, registration, questionnaire submission, verification, SAW calculation, CSV import, account management, and report printing, found that all cases passed. The system offers a transparent and reproducible way to prioritize candidates, while the final decision remains with the authorized agency. Future work should validate weights and rankings against field data.