Contextual Data Fusion and Explainable Analytics for Supporting Strategic Decision Making in Smart Information Systems Environments - Priyo Wibowo; Rudolf Sinaga (International Journal of Computer Technology and Science, 2024)
The increasing complexity and heterogeneity of data in Smart Information Systems pose significant challenges for effective decision-making. While data fusion techniques have been widely adopted to integrate multiple data sources, traditional fusion approaches often fail to consider contextual information, resulting in limited interpretability and reduced decision relevance. This study proposes a contextual data fusion approach that integrates heterogeneous data sources with contextual attributes, including temporal, spatial, and operational context, to enhance decision accuracy and robustness. The research employs a computational and experimental methodology involving data preprocessing, context encoding, multi-level data fusion, and performance evaluation. Experimental results demonstrate that the proposed approach outperforms single-source analysis and non-contextual data fusion in terms of accuracy, precision, recall, and F1-score, with only a marginal increase in computational cost. The findings confirm that incorporating context into the data fusion process significantly improves the quality and reliability of analytical outcomes. This study contributes to the development of intelligent and data-driven systems by highlighting the critical role of contextual awareness in supporting transparent and effective decision-making in Smart Information Systems.