Web-Based Decision Support System for Prosperous Family Classification Using the Analytical Hierarchy Process
Abstrak
Purpose: This study aimed to design and implement a web-based Decision Support System (DSS) for classifying prosperous families in Dusun Cibanban, Desa Gerning, Tegineneng District, Pesawaran Regency, Indonesia, using the Analytical Hierarchy Process (AHP) method. The system addresses the inefficiency and subjectivity inherent in the manual family welfare assessment process currently employed by local government administrators. Research Methodology: The system was developed using the waterfall methodology and the AHP method to assess welfare criteria based on national family welfare standards. It was implemented using PHP (CodeIgniter), JavaScript, and MySQL, with system functionality validated through testing of user and administrator modules. Results: The system successfully generated AHP-based welfare rankings and passed functional testing. Marsidi achieved the highest priority weight (0.1736), demonstrating the system’s ability to provide objective welfare classifications. Conclusions: The web-based AHP-DSS system provides a faster, more transparent, and more objective mechanism for prosperous family classification than the manual approach previously employed in Dusun Cibanban. The system successfully digitized the multi-criteria welfare assessment process and produced ranked outputs that are accessible via a standard web browser. Limitations: The study was conducted in a single sub-village (dusun) with 102 household heads, limiting the generalizability of the findings. The AHP criteria weights were determined through expert elicitation, rather than empirical validation. The system performance under high-concurrency conditions and with larger datasets was not evaluated. Contributions: This research contributes a validated web-based AHP-DSS implementation for community-level family welfare classification in an Indonesian rural village context, providing a replicable model for local government administrators seeking to modernize social welfare targeting through data-driven decision support.
Identifier
- DOI: https://doi.org/10.35912/jkbpm.v1i1.6763
- OpenAlex: https://openalex.org/W7169881704