<![CDATA[Many village-level poverty programs still depend on manual deliberation, which is slow to audit and difficult to reproduce across localities. This study addresses that gap by delivering an end-to-end, transparent implementation of the Weighted Product (WP) method for ranking poor households in Prunggahan Kulon, Tuban Regency. We assess whether a clearly specified WP pipeline complete with documented polarity (benefit/cost), normalized weights, and run logs can convert heterogeneous village records into reproducible preferences suitable for operational targeting. Household data supplied by the village and the Social Office were coded on a 0–1 scale for eight agreed criteria; expenditure (C2) was treated as a cost while others were benefits. Equal weights were used in this initial deployment for clarity and explainability. The method was implemented in a Laravel-based system that records bases, signed exponents, the multiplicative score , and normalized preferences . A five-household subset (A1–A5) is reported for illustration, with the full system supporting larger lists. The computation yielded a clear ordering (A4 > A1 > A2 > A3 > A5). The multiplicative rule preserved penalties for critical shortfalls and prevented strong indicators from masking severe deprivations, while the software artifacts ensured traceability from inputs to final . The dataset comprised 491 households encoded across eight criteria, with one cost criterion and seven benefits. Compared with prior WP applications, our contribution is an end-to-end, district-ready pipeline with explicit polarity, documented weights, and preserved run logs enabling third-party replication. This design measurably improves transparency and reproducibility for local poverty targeting.]]>
