<![CDATA[The increasing availability of hospital data through national health information systems offers significant opportunities for data-driven analysis. However, hospital datasets are often characterized by high dimensionality and numerous interrelated variables, which may lead to information redundancy, increased computational complexity, and reduced interpretability. This study proposes a feature engineering framework based on Exploratory Factor Analysis (EFA) to develop a structured representation of hospital data in Indonesia. Data were collected from the Hospital Information System (SIRS) managed by the Ministry of Health of the Republic of Indonesia, covering various aspects such as human resources, service capacity, healthcare services, and infrastructure. The proposed methodology includes data acquisition, pre-processing, and factor extraction using EFA. The extracted latent factors are expected to capture the underlying dimensions of hospital characteristics while reducing data complexity. The findings are expected to provide a more interpretable representation of hospital profiles and serve as a foundation for future hospital segmentation and machine learning-based healthcare analytics. The proposed framework may also support evidence-based decision-making in healthcare planning and resource management.]]>
Copyrights © 2026
