Implementasi Algoritma AHP Sebagai Sistem Penunjang Keputusan dalam Mengevaluasi Kelayakan Rehabilitasi Rumah Warga Berpenghasilan Rendah

Abstract

The availability of decent housing is one of the important indicators in improving the quality of life of the community, especially for residents with low incomes. In practice, the process of determining the eligibility of recipients of housing rehabilitation assistance is often still done manually and subjectively, thus potentially causing inaccuracy and injustice in decision making. The initiative stems from the growing need for systematic and objective tools to support government and social institutions in identifying eligible households for housing rehabilitation programs. In addressing this need, the Analytical Hierarchy Process (AHP) method is employed as a core decision-making framework within the DSS due to its strength in handling complex decision problems involving multiple criteria. The system incorporates various assessment criteria, including household income, structural condition of the house, number of dependents, ownership status, and age of the building. These criteria are structured hierarchically, and their relative importance is determined through pairwise comparisons facilitated by AHP. By applying this method, the system is capable of producing a weighted score for each applicant, thus enabling decision-makers to prioritize assistance based on quantifiable and transparent factors. The research includes both qualitative and quantitative analysis, starting from needs assessment, system requirement analysis, and user interface design, to the implementation of the AHP algorithm within the DSS. Results from case studies and prototype testing demonstrate that the system improves the accuracy and fairness of housing rehabilitation assessments compared to conventional manual evaluations. Moreover, it offers a user-friendly interface for stakeholders with varying technical backgrounds. In conclusion, the integration of AHP in the proposed DSS provides a reliable and scalable solution to support equitable housing policy decisions. Future developments may include integration with GIS for location-based analysis and mobile accessibility for field surveys.