Integrating FAST Methodology and Hybrid Recommendation for Government Asset Platforms - Fani Adi Setyawan; Erba Lutfina; Galuh Wilujeng Saraswati (SISTEMASI, 2026)
Optimizing Regional Owned Property (BMD) presents a critical challenge in modern government management, where numerous potential assets remain underutilized due to information asymmetry between the state and prospective tenants. Existing research indicates that current platforms predominantly focus on administrative recording rather than proactive commercialization, largely due to rigid regulations requiring strict appraisal-based pricing. This study proposes a novel conceptual integration to resolve this dichotomy by bridging strict public sector compliance with commercial-grade user experience. Instead of utilizing it merely as a development lifecycle tool, the FAST (Framework for the Application of Systems Technique) methodology is positioned as a regulatory guardrail to deterministically lock appraisal pricing and ensure strict adherence to Ministerial Regulations. Conversely, a Hybrid Recommendation System (merging Content-Based and Collaborative Filtering) is deployed explicitly at the tenant front-end to liberate and personalize the asset discovery process. These conflicting logic domains are seamlessly separated within a decoupled multi-tier architecture. Research findings reveal that the hybrid model successfully addresses the cold-start problem, achieving a top recommendation relevance score of 0.900 for newly listed assets. User Acceptance Testing (UAT) results demonstrate high efficacy, with a 94% acceptance rate from government administrators regarding regulatory logic validation and an 89% satisfaction score from public users regarding personalized recommendations. The explicit contribution of this study is a validated technological blueprint that harmonizes government legal compliance with commercial-grade discovery algorithms, effectively boosting Regional Original Revenue (PAD) optimization. In conclusion, this research confirms that encapsulating AI within a structured regulatory framework can transform rigid public sector assets into a responsive E-Business ecosystem. The explicit contribution of this study is a validated technological blueprint that harmonizes government legal compliance with commercial-grade discovery algorithms, boosting Regional Original Revenue (PAD) optimization, with future studies recommended to explore blockchain integration for auditability.