Implementation of Decision Support System for Illegal Cosmetic Detection in Papua Based Machine Learning - Burdam, Christian Victor; Arafah, Muhammad Nur; Burdam, Bernadeta Yusi Rosvilda (Journal of System and Computer Engineering, 2026)
Regulatory enforcement against illicit cosmetics in the Papua Special Autonomy regions remains constrained by profound geographical complexity and systemic data fragmentation. This study develops a robust Decision Support System (DSS) that bridges the gap between digital surveillance and legal accountability at BBPOM in Jayapura. By integrating the Simple Multi-Attribute Rating Technique (SMART) with a hybrid machine learning framework, the system operationalizes parameters derived from the Indonesian Health Law (No. 17 of 2023) and the Papua Special Autonomy Law (No. 2 of 2021). A high-fidelity dataset of 1,324 multi-source inspection records was utilized to train a hybrid ensemble architecture. Empirical results demonstrate that the Artificial Neural Network (ANN) achieves near-optimal performance, yielding a 99.62% accuracy and 99.90% F1-score, with a marginal error rate of 0.38%. The inclusion of PostGIS-driven spatial intelligence further enables real-time vulnerability mapping within a scalable Service-Oriented Architecture (SOA). Beyond its technical efficacy, this research contributes a novel paradigm for localized law enforcement, successfully unifying regional regulatory mandates with advanced predictive analytics to safeguard public health in marginalized frontiers.