Rising urban mobility, intensive anthropogenic activity, and limitations in conventional monitoring systems capable of delivering continuous spatial coverage have turned urban air pollution into a pressing environmental concern. Existing air-quality prediction approaches tend to depend on single-source observations or are built primarily for large metropolitan areas, which limits how well they apply to secondary cities with localized pollution dynamics. To address this, the study introduces an Urban Environmental Intelligence Framework that brings together multi-source air quality, climate, spatial, temporal, and air-quality information for urban air-quality prediction. Evaluation of the framework was conducted using hourly observations of pollutant concentrations (PM2.5, NO2, and CO), mobility indicators (traffic volume, vehicle count, average speed, and congestion index), meteorological variables, and urban spatial attributes, collected from Lhokseumawe, Indonesia. A multi-source XGBoost model was benchmarked against Single-Source XGBoost, Random Forest, and LSTM models using a chronological data partition and multiple evaluation metrics, including MAE, RMSE, MAPE, and R². The proposed framework achieved the strongest predictive performance across all target variables evaluated, attaining an R² of 0.912 and an RMSE of 5.84 µg/m³ for PM2.5 prediction. Ablation analysis confirmed that temporal and mobility information contributed most substantially to prediction accuracy, while climate and spatial variables provided complementary contextual value. Feature interpretation further revealed that traffic intensity, historical pollutant levels, and meteorological conditions were the dominant drivers of urban pollution outcomes. Overall, the proposed framework offers an effective environmental approach for supporting short-term air-quality forecasting, pollution hotspot identification, and evidence-based urban environmental management in secondary cities
S. Sibuea, Yohanes Bowo Widodo· International Journal of Eng...· 0 citations
The increasing volume and complexity of institutional documents in public organizations create challenges in accessing reliable knowledge for administrative processes and evidence-based decision-making. Conventional knowledge management systems often rely on keyword-based retrieval, while standalone Large Language Models (LLMs) may generate inaccurate responses when processing domain-specific institutional information. This study proposes a domain-specific Retrieval-Augmented Generation (RAG) framework to enhance institutional knowledge management and AI-assisted decision support in public-sector organizations. The framework was developed using a Design Science Research approach with Universitas Malikussaleh as a case study. The proposed architecture integrates institutional knowledge base construction, semantic retrieval, grounded language generation, and source attribution mechanisms. A knowledge base comprising 416 official institutional documents was developed through document preprocessing, semantic chunking, embedding generation, and vector database indexing. The framework was evaluated using 200 institutional queries based on retrieval performance, response quality, explainability, and system efficiency metrics. The results demonstrate effective retrieval capability, achieving Precision@5 of 0.884, Recall@5 of 0.921, and Mean Reciprocal Rank of 0.895. Generated responses achieved 94.6% factual accuracy, 91.8% contextual relevance, and 96.5% source attribution accuracy, while the hallucination rate was reduced to 3.2%. Furthermore, the framework achieved an average response latency of 1.18 seconds, indicating practical feasibility for institutional applications. These findings demonstrate that integrating semantic retrieval with grounded LLM generation can improve knowledge accessibility, transparency, and reliability for AI-assisted decision support in public organizations. The proposed framework provides a practical foundation for trustworthy institutional knowledge services and supports more efficient, explainable, and evidence-based administrative decision-making across diverse institutional contexts
Yohanes Bowo Widodo· International Journal of Eng...· 0 citations
