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All Journal The IC-BESTS (International Conference on Business, Economics, Technology, and Social Sciences)
Yusa Inderapermana
Research and Development Agency of Cirebon Regency

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EXPLAINABLE AI-BASED DECISION SUPPORT SYSTEM FOR MANGROVE RESTORATION SITE SELECTION IN CIREBON REGENCY Yusa Inderapermana; Ayubella Anggraini Leksono; Yuliana Susilowati
IC-BESTS: International Conference on Business, Economics, Technology, and Social Sciences 2026: The IC-BESTS (International Conference on Business, Economics, Technology, and Social Sciences
Publisher : POLITEKNIK LP3I JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34127/icbests.v1i1.165

Abstract

Mangrove restoration is a global priority for enhancing coastal resilience, conserving biodiversity, and increasing blue carbon sequestration. However, identifying suitable restoration sites remains challenging due to the complex interactions among environmental, ecological, and anthropogenic factors. This study proposes an Explainable Artificial Intelligence (XAI)-based framework that integrates remote sensing and geospatial data to support transparent and evidence-based mangrove restoration planning. Multi-source spatial datasets, including Sentinel-2 imagery, digital elevation models, land use and land cover, tidal inundation, hydrological connectivity, soil characteristics, coastline dynamics, proximity to rivers and settlements, and historical mangrove distribution, were analyzed using Random Forest and Extreme Gradient Boosting (XGBoost) models. Model predictions were interpreted using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to identify the relative contribution of each predictor. Simulation results for 15 candidate restoration zones in Kapetakan, Pangenan, Mundu, and Losari districts allocated a budget of IDR 902.13 million for restoring 46.86 ha. Three zones were recommended as optimal restoration sites with high success probabilities (0.629–0.954), six zones were excluded due to budget limitations, and six were rejected because of ecological constraints or predicted success probabilities below 0.40. The proposed framework enhances model transparency, strengthens stakeholder confidence, and supports informed coastal planning by providing interpretable, data-driven recommendations for sustainable mangrove restoration and climate adaptation
SPATIO-TEMPORAL MONITORING OF COASTAL LAND COVER CHANGE USING REMOTE SENSING AND ARTIFICIAL INTELLIGENCE IN CIREBON REGENCY Yuliana Susilowati; Ayubella Anggraini Leksono; Yusa Inderapermana
IC-BESTS: International Conference on Business, Economics, Technology, and Social Sciences 2026: The IC-BESTS (International Conference on Business, Economics, Technology, and Social Sciences
Publisher : POLITEKNIK LP3I JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34127/icbests.v1i1.241

Abstract

Coastal regions are highly dynamic environments that experience continuous land cover changes driven by urban expansion, aquaculture development, agricultural activities, shoreline erosion, and ecosystem degradation. Monitoring these changes is essential for supporting sustainable coastal management, environmental conservation, and climate adaptation. This study evaluates the integration of remote sensing and artificial intelligence (AI) for monitoring land cover dynamics in the coastal area of Cirebon Regency, Indonesia, during the period 2015–2025. Multi-temporal Landsat satellite imagery was processed through atmospheric and geometric corrections, cloud masking, image compositing, and feature extraction. Spectral indices, including the Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Mangrove Vegetation Index (MVI), were employed to improve the discrimination of water bodies, mangroves, built-up areas, and other vegetation. A Random Forest algorithm was applied to classify land cover and detect spatial and temporal changes with improved accuracy and efficiency compared with conventional approaches. The results indicate a continuous expansion of built-up areas accompanied by a substantial decline in mangrove forests and water bodies, reflecting increasing anthropogenic pressure on coastal ecosystems. Although other vegetation exhibited a moderate increase, it cannot compensate for the ecological functions lost through mangrove degradation, particularly in coastal protection, biodiversity conservation, and blue carbon storage. These findings provide valuable baseline information for sustainable coastal spatial planning, ecosystem restoration, disaster risk reduction, and environmental impact assessment of future infrastructure developments, including the Giant Sea Wall (GSW), while demonstrating the effectiveness of AI-assisted remote sensing for long-term coastal resource management in Indonesia
Co-Authors Ayubella Anggraini Leksono Ayubella Anggraini Leksono Yuliana Susilowati