Integrasi Explainable Artificial Intelligence (XAI) pada WebGIS untuk Audit Kepatuhan Ruang Terbuka Hijau di Provinsi Lampung - Fiqih Satria; M Husaini (Politeknik Caltex Riau, 2026)
The acceleration of development in Lampung Province has led to significant land-use conversion, resulting in a decline in environmental carrying capacity and non-compliance with minimum Green Open Space (GOS) requirements. This problem is exacerbated by the limitations of spatial monitoring systems, which remain descriptive in nature and are unable to provide predictive analysis or transparency in decision-making. This study aims to develop Lampung Scientific GIS, a WebGIS platform based on Explainable Artificial Intelligence (XAI) to conduct GOS compliance audits and ecological load analysis automatically and in an interpretable manner. The methods employed include data extraction from ESRI Sentinel-2 Land Cover satellite imagery using zonal statistics techniques, the development of a three-layer WebGIS architecture, and the implementation of a Rule-Based Explainable Artificial Intelligence (XAI) model to evaluate compliance with regulations and calculate the Ecological Burden Index. The research results indicate that urban areas such as Bandar Lampung City and Metro City are non-compliant with the 30% RTH mandate and face high ecological pressure. The developed system is capable of generating transparent risk assessments and data-driven policy recommendations. This research contributes to the development of an accountable spatial decision support system through the integration of WebGIS and XAI for sustainable regional planning. The novelty of this research lies in the integration of real-time, Rule-Based Explainable Artificial Intelligence (XAI) within a WebGIS environment, enabling spatial compliance audits that are not only descriptive but also interpretable, prescriptive, and transparent in supporting data-driven decision-making.