Design and Evaluation of a Decision Support System for Classifying Tourism Site Crowding and Recommending Governance Responses in Bunaken National Park

Abstract

Effective governance of marine protected areas (MPAs) requires reliable mechanisms to translate multidimensional ecological and social data into coordinated institutional action. Despite widespread adoption of carrying capacity frameworks, a significant "implementation gap" persists between theoretical conservation thresholds and operational decision-making at the site level. This study addresses that gap by designing, implementing, and evaluating a Decision Support System (DSS) artifact tailored for Bunaken National Park (BNP), Indonesia. Grounded in Design Science Research (DSR) principles, the artifact employs a deterministic, rule-based classification engine that processes four normalized input dimensions visitor density, social carrying capacity, infrastructure load, and governance readiness to compute a Composite Crowding Index (CCI). The CCI is mapped through an explicit IF-THEN rule engine to four crowding categories (Low, Moderate, High, Extreme), each linked to a validated governance action package. A deterministic rule-based approach was chosen over probabilistic or machine-learning alternatives to ensure full decision traceability, which is a non-negotiable requirement for public-sector governance. System robustness was evaluated through structured scenario testing across 140 logic-coverage cases, assessed against four criteria: output consistency (100%), expert rule alignment (97.8%), decision traceability (100%), and processing efficiency (<1.15 seconds per scenario). The artifact successfully automates the mapping of site-level crowding status to discrete, auditable governance actions. The theoretical contribution lies in formalizing subjective management reasoning into a transparent, reproducible DSS that bridges sustainability science and institutional practice in high-pressure marine tourism environments.