PENGEMBANGAN REPRESENTASI DATA RUMAH SAKIT DI INDONESIA MENGGUNAKAN EXPLORATORY FACTOR ANALYSIS
DOI:
https://doi.org/10.32546/jusin.v7i1.3509Keywords:
Eksplorasi, Dataset, Rumah Sakit, Exploratory Factor, Machine LearningAbstract
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.
References
Abodayeh, A. H. (2023). Web scraping for data analytics: A beautifulsoup implementation. sixth international conference of women in data science at prince Sultan University. IEEE.
Cavina, P., & et al. (2026). Beyond tabular data: Feature extraction and selection for high-dimensional data. Array.
Direktorat Jenderal Kesehatan Lanjutan. (2025, December). Dashboard RS Online. Retrieved from Sistem Informasi Rumah Sakit: https://sirs.kemkes.go.id/fo/home/dashboard_rs?id=0
Dong, E., Sun 4, X., Xu , T., Zhang, S., Wang, T., & et al. (2023). Measuring the inequalities in healthcare resource in facility and workforce: A longitudinal study in China. National Library of Medicine.
Finch, W. (2023). A Comparison of Methods for Determining the Number of Factors to Retain with Exploratory Factor Analysis of Dichotomous Data. Psych.
G. Manikandan, S. A. (2021). An efficient feature selection framework based on information theory for high dimensional data. Applied Soft Computing.
Hammad, A. R. (2022). PERKEMBANGAN RUMAH SAKIT DI INDONESIA DI ERA DISRUPTIF. Jurnal Keperawatan Merdeka, 2(1), 1-6.
Howard, M. C. (2023). A systematic literature review of exploratory factor analyses in management. Journal of Business Research.
Jiarui Han, J. H. (2025). Spatial accessibility evaluation and clustering analysis of primary healthcare within 15 min circles in a northern Chinese megacity. Scientific Report.
Jondya, A. G., Rahma, A. M., & Sophia, T. (2024). Enhancing Public Health Records: Development of Death Certificate Management System. 4th International Conference on Electrical Engineering and Informatics (ICon EEI). Pekanbaru.
Kementerian Kesehatan Republik Indonesia. (2020). Peraturan Menteri Kesehatan Republik Indonesia Nomor 3 Tahun 2020. Retrieved April 16, 2026, from Peraturan Badan Pemeriksa Keuangan: https://peraturan.bpk.go.id/Details/152506/permenkes-no-3-tahun-2020
Marengo, A., Santamato, V., & Iacoviello, M. (2025). Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals. IEEE Access, 13.
Mirna Raafiana, H. A. (2025). KESIAPAN RUMAH SAKIT DALAM IMPLEMENTASI KEBIJAKAN KELAS RAWAT INAP STANDAR (KRIS) JKN : LITERATURE REVIEW. Syntax Literate: Jurnal Ilmiah Indonesia, 10(1).
Mumuni, A., & Mumuni, F. (2025). Automated data processing and feature engineering for deep learning and big data applications: A survey. Journal of Information and Intelligence, 113-153.
Ray, P., Reddy, S. S., & Banerjee, T. (2021). Various dimension reduction techniques for high dimensional data analysis: a review. Artificial Intelligence Review, 3473–3515.
Shu, L., Huang, K., & et al. (2021). Feature selection using autoencoders with Bayesian methods to high-dimensional data. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology.
Thao, N., Tan, N., & Mai , T. (2022). KMO and Bartlett's Test for Components of Workers' Working Motivation and Loyalty at Enterprises in Dong Nai Province of Vietnam. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
Verdonck, T., Baesens, B., Óskarsdóttir, M., & vanden Broucke, S. (2024). Special issue on feature engineering editorial. Machine Learning, 3917-3928.
